File size: 21,733 Bytes
53eb596 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f434122e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:55:01.376569Z",
"iopub.status.busy": "2025-03-25T07:55:01.376303Z",
"iopub.status.idle": "2025-03-25T07:55:01.542902Z",
"shell.execute_reply": "2025-03-25T07:55:01.542550Z"
}
},
"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 = \"Lupus_(Systemic_Lupus_Erythematosus)\"\n",
"cohort = \"GSE148810\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)\"\n",
"in_cohort_dir = \"../../input/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE148810.csv\"\n",
"json_path = \"../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "0ec60231",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d90f3b8c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:55:01.544420Z",
"iopub.status.busy": "2025-03-25T07:55:01.544265Z",
"iopub.status.idle": "2025-03-25T07:55:01.632339Z",
"shell.execute_reply": "2025-03-25T07:55:01.632018Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Gene expression of skin biopsie samples from juvenile myositis (JM) and childhood-onset lupus (cSLE).\"\n",
"!Series_summary\t\"Skin inflammaton heralds systemic disease in juvenile myositis (JM), yet we lack an understanding of pathogenic mechanisms driving skin inflammation in JM. The goal of this study is to define cutaneous gene expression signatures in JM and identify key genes and pathways that differentiate skin disease in JM from childhood-onset SLE (cSLE).\"\n",
"!Series_overall_design\t\"Formalin formalin-fixed paraffin-embedded (FFPE) skin biopsy samples from JM, cSLE, and control (HC) patients were used to perform Affymetrix ST 2.1 microarray analysis and determine differentially expressed genes (DEGs; q-value ≤ 5%) between patient groups.\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['tissue: Skin biopsy'], 1: ['disease: JM Lesional skin', 'disease: JM Non-lesional skin', 'disease: cSLE skin lesion', 'disease: Normal skin']}\n"
]
}
],
"source": [
"from tools.preprocess import *\n",
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. 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",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
"sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
"\n",
"# 4. 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"
]
},
{
"cell_type": "markdown",
"id": "3af8cde5",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ca41a1a0",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:55:01.633542Z",
"iopub.status.busy": "2025-03-25T07:55:01.633427Z",
"iopub.status.idle": "2025-03-25T07:55:01.638225Z",
"shell.execute_reply": "2025-03-25T07:55:01.637921Z"
}
},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import pandas as pd\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# 1. Gene Expression Data Availability\n",
"# Based on the background information, this dataset appears to contain gene expression data\n",
"# from microarray analysis (Affymetrix ST 2.1)\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# For trait: Looking at the sample characteristics dictionary, disease information is in row 1\n",
"trait_row = 1\n",
"\n",
"# Age information is not available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# Gender information is not available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion Functions\n",
"def convert_trait(value: str) -> Optional[int]:\n",
" \"\"\"Convert trait value to binary (1 for SLE, 0 for others)\"\"\"\n",
" if value is None:\n",
" return None\n",
" \n",
" # Extract the value part after the colon if present\n",
" if \":\" in value:\n",
" value = value.split(\":\", 1)[1].strip()\n",
" \n",
" # Check if the value indicates SLE (Systemic Lupus Erythematosus)\n",
" if \"cSLE\" in value:\n",
" return 1 # SLE positive\n",
" else:\n",
" return 0 # Not SLE (JM or Normal)\n",
"\n",
"def convert_age(value: str) -> Optional[float]:\n",
" \"\"\"Convert age value to continuous numeric\"\"\"\n",
" # Not applicable as age data is not available\n",
" return None\n",
"\n",
"def convert_gender(value: str) -> Optional[int]:\n",
" \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
" # Not applicable as gender data is not available\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Validate and save cohort info for initial filtering\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",
"# Since trait_row is not None, we need to extract clinical features\n",
"if trait_row is not None:\n",
" # Load the clinical data\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",
" \n",
" # Extract 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 extracted features\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Preview of selected clinical features:\")\n",
" print(preview)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the extracted features\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": "68bf01bf",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3484b945",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:55:01.639317Z",
"iopub.status.busy": "2025-03-25T07:55:01.639205Z",
"iopub.status.idle": "2025-03-25T07:55:01.765413Z",
"shell.execute_reply": "2025-03-25T07:55:01.765020Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n",
"No subseries references found in the first 1000 lines of the SOFT file.\n",
"\n",
"Gene data extraction result:\n",
"Number of rows: 29635\n",
"First 20 gene/probe identifiers:\n",
"Index(['100009613_at', '100009676_at', '10000_at', '10001_at', '10002_at',\n",
" '100033411_at', '100033413_at', '100033422_at', '100033423_at',\n",
" '100033424_at', '100033425_at', '100033426_at', '100033427_at',\n",
" '100033428_at', '100033430_at', '100033431_at', '100033432_at',\n",
" '100033434_at', '100033435_at', '100033436_at'],\n",
" dtype='object', name='ID')\n"
]
}
],
"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": "4424abe6",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "83115426",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:55:01.766711Z",
"iopub.status.busy": "2025-03-25T07:55:01.766586Z",
"iopub.status.idle": "2025-03-25T07:55:01.768533Z",
"shell.execute_reply": "2025-03-25T07:55:01.768238Z"
}
},
"outputs": [],
"source": [
"# The gene identifiers consist of numbers followed by \"_at\" suffix, which is typical for Affymetrix microarray probes\n",
"# These are not standard human gene symbols and need to be mapped to proper gene symbols\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "5da3e492",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "99da98a1",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:55:01.769689Z",
"iopub.status.busy": "2025-03-25T07:55:01.769573Z",
"iopub.status.idle": "2025-03-25T07:55:02.792213Z",
"shell.execute_reply": "2025-03-25T07:55:02.791809Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene annotation preview:\n",
"{'ID': ['1_at', '10_at', '100_at', '1000_at', '10000_at'], 'SPOT_ID': ['1', '10', '100', '1000', '10000'], 'DESCRIPTION': ['alpha-1-B glycoprotein', 'N-acetyltransferase 2', 'adenosine deaminase', 'cadherin 2', 'AKT serine/threonine kinase 3'], 'ORF': ['A1BG', 'NAT2', 'ADA', 'CDH2', 'AKT3']}\n"
]
}
],
"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": "94df63f2",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "979b7584",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:55:02.793643Z",
"iopub.status.busy": "2025-03-25T07:55:02.793508Z",
"iopub.status.idle": "2025-03-25T07:55:02.916704Z",
"shell.execute_reply": "2025-03-25T07:55:02.916286Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview:\n",
"{'ID': ['1_at', '10_at', '100_at', '1000_at', '10000_at'], 'Gene': ['A1BG', 'NAT2', 'ADA', 'CDH2', 'AKT3']}\n",
"\n",
"Gene expression data after mapping:\n",
"Number of genes: 23124\n",
"First few genes:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
" 'A4GALT', 'A4GNT', 'AA06'],\n",
" dtype='object', name='Gene')\n"
]
}
],
"source": [
"# 1. Identify columns for gene mapping \n",
"# From the preview, it seems 'ID' contains probe identifiers and 'ORF' contains gene symbols\n",
"probe_col = 'ID' # Column containing probe identifiers\n",
"gene_col = 'ORF' # Column containing gene symbols\n",
"\n",
"# 2. Extract gene mapping dataframe\n",
"gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(gene_mapping))\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene-level expression data\n",
"# The library function 'apply_gene_mapping' will handle the many-to-many relationship\n",
"gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
"print(\"\\nGene expression data after mapping:\")\n",
"print(f\"Number of genes: {len(gene_data)}\")\n",
"print(\"First few genes:\")\n",
"print(gene_data.index[:10])\n"
]
},
{
"cell_type": "markdown",
"id": "608ab528",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "40ae519f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-03-25T07:55:02.918097Z",
"iopub.status.busy": "2025-03-25T07:55:02.917963Z",
"iopub.status.idle": "2025-03-25T07:55:13.749013Z",
"shell.execute_reply": "2025-03-25T07:55:13.748611Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 10 gene indices before normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 10 gene indices after normalization: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1', 'A4GALT', 'A4GNT', 'AA06']\n",
"Shape of normalized gene data: (22856, 30)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved normalized gene data to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv\n",
"Saved clinical data to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE148810.csv\n",
"Shape of linked data: (30, 22857)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of linked data after handling missing values: (30, 22857)\n",
"For the feature 'Lupus_(Systemic_Lupus_Erythematosus)', the least common label is '1.0' with 7 occurrences. This represents 23.33% of the dataset.\n",
"The distribution of the feature 'Lupus_(Systemic_Lupus_Erythematosus)' in this dataset is fine.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved processed linked data to ../../output/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810.csv\n"
]
}
],
"source": [
"# 1. Normalize gene symbols in the gene expression data\n",
"print(f\"Top 10 gene indices before normalization: {gene_data.index[:10].tolist()}\")\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"print(f\"Top 10 gene indices after normalization: {normalized_gene_data.index[:10].tolist()}\")\n",
"print(f\"Shape of normalized gene data: {normalized_gene_data.shape}\")\n",
"\n",
"# Create directory for gene data file if it doesn't exist\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"# Save the normalized gene data\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Saved normalized gene data to {out_gene_data_file}\")\n",
"\n",
"# 2. Extract clinical features using the clinical data from step 1\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# Extract clinical features using the convert_trait function from step 2\n",
"selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=1, # From step 2\n",
" convert_trait=convert_trait,\n",
" age_row=None,\n",
" convert_age=None,\n",
" gender_row=None,\n",
" convert_gender=None\n",
")\n",
"\n",
"# Save 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)\n",
"print(f\"Saved clinical data to {out_clinical_data_file}\")\n",
"\n",
"# 3. Link clinical and genetic data\n",
"linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_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 = handle_missing_values(linked_data, trait)\n",
"print(f\"Shape of linked data after handling missing values: {linked_data.shape}\")\n",
"\n",
"# 5. Determine if the trait and demographic features are biased\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
"\n",
"# 6. Validate the dataset and save cohort information\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=\"Dataset contains gene expression data from juvenile myositis (JM) and childhood-onset lupus (cSLE) skin biopsies.\"\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": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|