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Can you help me add error handling if some images don't have corresponding masks?<|end|> <|assistant|>Sure! We can add error handling by checking if each patient ID found in `image_paths` exists in `mask_paths`. Here's how you could do it: python def create_csv(): '''Creates CSV file listing all images''' # Get all image file paths image_paths = glob.glob(os.path.join(raw_data_path,'*')) # Get all mask file paths mask_paths = glob.glob(os.path.join(mask_data_path,'*')) # Extract patient IDs using string operations image_patient_ids = pd.Series(image_paths).apply(lambda x: x.split('/')[-1].split('_')[0]) mask_patient_ids = pd.Series(mask_paths).apply(lambda x: x.split('/')[-1].split('_')[0]) # Create dictionaries mapping patient IDs to their paths image_dict = image_patient_ids.to_dict() mask_dict = mask_patient_ids.to_dict() # Check for missing masks or images missing_masks = [pid for pid in image_dict if pid not in mask_dict] missing_images = [pid for pid in mask_dict if pid not in image_dict] if missing_masks: print(f"Warning: Missing masks for patients {missing_masks}") if missing_images: print(f"Warning: Missing images for patients {missing_images}") # Filter only matching patient IDs common_patient_ids = set(image_dict.keys()).intersection(set(mask_dict.keys())) filtered_image_paths = [image_dict[pid] for pid in common_patient_ids] filtered_mask_paths = [mask_dict[pid] for pid in common_patient_ids] # Create dataframes image_df = pd.DataFrame({'path': filtered_image_paths}) mask_df = pd.DataFrame({'path': filtered_mask_paths}) # Concatenate dataframes along columns df = pd.concat([image_df.assign(key=0), mask_df.assign(key=1)], axis=1).drop('key', axis=1) df.to_csv('all_image_data.csv', index=False) This will print warnings about any missing masks or images but only write entries where both exist.<|end|> <|user|>Can we speed this up with parallel processing? Reading large directories takes too long.<|end|> <|assistant|>Certainly! We can use `concurrent.futures.ThreadPoolExecutor` to parallelize reading file paths from directories. python import concurrent.futures def get_file_paths(directory): return glob.glob(os.path.join(directory,'*')) def create_csv(): '''Creates CSV file listing all images''' with concurrent.futures.ThreadPoolExecutor() as executor: future_image_paths = executor.submit(get_file_paths, raw_data_path) future_mask_paths = executor.submit(get_file_paths, mask_data_path) image_paths = future_image_paths.result() mask_paths = future_mask_paths.result() # Extract patient IDs using string operations image_patient_ids = pd.Series(image_paths).apply(lambda x: x.split('/')[-1].split('_')[0]) mask_patient_ids = pd.Series(mask_paths).apply(lambda x: x.split('/')[-1].split('_')[0]) # Create dictionaries mapping patient IDs to their paths image_dict = image_patient_ids.to_dict() mask_dict = mask_patient_ids.to_dict() # Check for missing masks or images missing_masks = [pid for pid in image_dict if pid not in mask_dict] missing_images = [pid for pid in mask_dict if pid not in image_dict] if missing_masks: print(f"Warning: Missing masks for patients {missing_masks}") if missing_images: print(f"Warning: Missing images for patients {missing_images}") # Filter only matching patient IDs common_patient_ids = set(image_dict.keys()).intersection(set(mask_dict.keys())) filtered_image_paths = [image_dict[pid] for pid in common_patient_ids] filtered_mask_paths = [mask_dict[pid] for pid in common_patient_ids] # Create dataframes image_df = pd.DataFrame({'path': filtered_image_paths}) mask_df = pd.DataFrame({'path': filtered_mask_paths}) # Concatenate dataframes along columns df = pd.concat([image_df.assign(key=0), mask_df.assign(key=1)], axis=1).drop('key', axis=1) df.to_csv('all_image_data.csv', index=False) This approach uses threads to read files concurrently which should speed up processing significantly.<|end|> <|user|>Instead of simple string operations, let's use regex to extract patient IDs.<|end|> <|assistant|>Great idea! Using regular expressions will make extracting patient IDs more robust. python import re def extract_patient_id(filepath): match = re.search(r'(d+)_', filepath.split('/')[-1]) return match.group(1) if match else None def create_csv(): '''Creates CSV file listing all images''' with concurrent.futures.ThreadPoolExecutor() as executor: future_image_paths = executor.submit(get_file_paths, raw_data_path) future_mask_paths = executor.submit(get_file_paths, mask_data_path) image_paths = future_image_paths.result() mask_paths = future_mask_paths.result() # Extract patient IDs using regex image_patient_ids = pd.Series(image_paths).apply(extract_patient_id) mask_patient_ids = pd.Series(mask_paths).apply(extract_patient_id) # Create dictionaries mapping patient IDs to their paths image_dict = dict(zip(image_patient_ids.dropna(), image_paths)) mask_dict = dict(zip(mask_patient_ids.dropna(), mask_paths)) # Check for missing masks or images missing_masks = [pid for pid in image_dict if pid not in mask_dict] missing_images = [pid for pid in mask_dict if pid not in image_dict] if missing_masks: print(f"Warning: Missing masks for patients {missing_masks}") if missing_images: print(f"Warning: Missing images for patients {missing_images}") # Filter only matching patient IDs common_patient_ids = set(image_dict.keys()).intersection(set(mask_dict.keys())) filtered_image_paths = [image_dict[pid] for pid in common_patient_ids] filtered_mask_paths = [mask_dict[pid] for pid in common_patient_ids] # Create dataframes image_df = pd.DataFrame({'path': filtered_image_paths}) mask_df = pd.DataFrame({'path': filtered_mask_paths}) # Concatenate dataframes along columns df = pd.concat([image_df.assign(key=0), mask_df.assign(key=1)], axis=1).drop('key', axis=1) df.to_csv('all_image_data.csv', index=False) This code uses regex (`re.search`) to extract numeric patient IDs from filenames.<|end|> <|user|>I want dynamic column addition based on metadata extracted from filenames. For example, add separate columns if filename contains 'T1' or 'T2'. How do we do this?<|end|> <|assistant|>We can extract additional metadata using regex groups within our `extract_metadata` function and then dynamically add columns based on this metadata. Here's how you can achieve that: python def extract_metadata(filepath