Parsing Metadata from Filenames

In neuroscience, we often work with large datasets where file naming conventions encode crucial metadata, helping to find the relevant files for a given analysis. String manipulation–the extraction of structured data from text written in a machine-readable pattern– makes it possible to extract this information efficiently, streamlining data processing workflows.

Section 1: Extracting Metadata from Fixed-Length Strings using String Slicing

These examples provide a clear understanding of how to use slicing to extract specific substrings from a larger string based on their positions. This is a powerful tool in string manipulation, often used in data processing and analysis.

This researcher had a rule for her filenames: she would store session metadata in fixed-length strings, with information always in the same order:

• Subject Name: 6 Characters
• Date: 8 Characters
• Treatment Group: 7 Characters:
• Session Number: 5 Characters (“sess” and then the number)

That way, when she later needed the information, she could extract it from the filename just by slicing it!

Exercises

Example: What subject name’s data is in this file?

fname = "Arthur20241008controlsess1.txt"   # Filename convention: Subject, Date, Group, Session
fname[:6]

'Arthur'

Exercise: What group is this subject in?

fname = "Arthur20241008controlsess1.txt"   # Filename convention: Subject, Date, Group, Session
fname[14:21]

'control'

Exercise: What Session number was this? (Note: after extracting the number, turn it from a string into an int with the int() function.)

fname = "Arthur20241008controlsess1.txt"   # Filename convention: Subject, Date, Group, Session
int(fname[25:26])
fname[-5]

'1'

Exercise: Extract all four metadata variables from the following file and put them into their own variables (note that the subject has fewer than 6 characters in their name. After slicing the data, you can replace the underscore characters with “empty strings” by using the replace() method on strings (e.g. "name__".replace('_', '')):

fname = "Joe___20241009experimsess1.txt"  # Filename convention: Subject, Date, Group, Session
subject, date, group, sess = fname[:6].replace('_', ''), fname[6:14], fname[14:21], int(fname[25])
subject, date, group, sess

('Joe', '20241009', 'experim', 1)

Exercise: Make a dictionary with the keys “Subject”, “Date”, “Group”, and “SessionNum” with the data from this filename:

fname = "Arthur20241008controlsess1.txt"   # Filename convention: Subject, Date, Group, Session
session = {
    "Subject": fname[:6], 
    "Date": fname[6:14], 
    "Group": fname[14:21],
    "SessionNum": int(fname[25]),
}
session

{'Subject': 'Arthur', 'Date': '20241008', 'Group': 'control', 'SessionNum': 1}

Demo: Building a table of metadata usually has the following steps, which can be done in a loop:

1. Extract data into a dictionary
2. Append the dictionary into a list of dictionaries
3. Change the list of dictionaries into a data frame (the table)

Example: Fill in the missing data extraction code for the filenames below to make a session table. Include the original filename in its own column, to make finding the file later simpler:

fnames = ["a2.txt", "b3.txt"]

all_sessions = []
for fname in fnames:
    session = {
        "Letter": fname[0],
        "Number": int(fname[1]),
        "Filename": fname,
    }
    all_sessions.append(session)

all_sessions

[{'Letter': 'a', 'Number': 2, 'Filename': 'a2.txt'},
 {'Letter': 'b', 'Number': 3, 'Filename': 'b3.txt'}]

Example: Use the Pandas library to turn this list of dictionaries into a table:

import pandas as pd
df = pd.DataFrame(all_sessions)
df

Section 2: Technique: Variable-Length, Character-Separated Strings (string splitting)

In this section, we explore a flexible and practical approach to handling filenames in data management: variable-length, character-separated strings. This method is particularly useful in scenarios where the length of data attributes varies significantly, such as with names of different lengths. By adopting a convention where each piece of metadata in the filename is separated by a specific character (like an underscore “_”), researchers can accommodate varying data lengths effortlessly. This technique is common in many fields, including neuroscience, where data files often need to contain detailed, yet neatly organized, metadata. For example:

<Subject>_<Date>_<SessionCondition>_<SessionNum>.<FileExtension>

The filename convention here uses underscores to separate different data elements and a dot to denote the file extension. For example, a filename like “Joe_20230101_Control_01.txt” is easily parsed into its constituent parts: subject name, date, session condition, and session number. You’ll learn to use the split method in Python, which is a straightforward way to divide a string into a list of substrings based on a specified separator.

Exercises

Example: The filename convention here is <Subject>_<Date>_<Group>_<SessionNum>.<FileExtension>. Extract the date this filename into its own variables:

fname = "Arthur_20241008_control_1.txt"
base, ext = fname.split('.')
data = base.split('_')
date = data[1]
date

'20241008'

Exercise: Extract the Group from this filename into its own variables:

base = fname.split('.')[0]

fname = "Arthur_20241008_control_1.txt"
base, ext = fname.split('.')
data = base.split('_')
group = data[2]
group

'control'

Exercise: Extract all the data from this filename into a dictionary:

fname = "Arthur_20241008_control_1.txt"
base, ext = fname.split('.')

subject = base.split('_')[0]
date = base.split('_')[1]
subject, date

('Arthur', '20241008')

subject, date, session, num = base.split('_')
data = {"Subject": subject, "Date": date, "Group": group, "SessionNum": num}
data

{'Subject': 'Arthur',
 'Date': '20241008',
 'Group': 'control',
 'SessionNum': '1'}

Section 3: Self-Describing Metadata: Getting Key-Values Directly from a String

Searching the String for Patterns using index()

In this section, we focus on extracting self-describing metadata from strings using pattern searching, a technique especially useful in scenarios where data is embedded within a string in a predictable manner. This method is crucial when dealing with filenames or text data where specific metadata follows a known pattern or a set keyword. Neuroscience researchers often encounter such situations, for instance, when filenames or data entries include coded information like session numbers or participant IDs embedded within them.

Here’s the completed table with additional examples demonstrating how to use the index() method for finding specific patterns in strings and extracting relevant information:

The following Filenames have a different file naming convention:

<SessionID>_<BrainRegion>-d1=<ImageHeightInPixels>,d2=<ImageWidthInPixels>.<FileExtension>

Exercises

Example: Using the index to find the d1= section from this filename, extract the image height:

fname = "242_CA1-d1=720,d2=1080.tif"
start_idx = fname.index("d1=") + len("d1=")
end_idx = fname.index(",")
height = int(fname[start_idx:end_idx])
height

720

Exercise: Using the index to find the d2= section from this filename, extract the image width:

fname = "2045_CA3-d1=1080,d2=720.tif"

fname = "2045_CA3-d1=1080,d2=720.tif"
start_idx = fname.index("d2=") + len("d2=")
end_idx = fname.index(".")
width = int(fname[start_idx:end_idx])
width

720

Exercise: Using the index to find the _ section from this filename, extract the brain region:

fname = "24_DG-d1=720,d2=720.tif"

fname = "24_DG-d1=720,d2=720.tif"
start_idx = fname.index("_") + len("_")
end_idx = fname.index('-')
brain_region = fname[start_idx:end_idx]
brain_region

'DG'

Demo

::: {#exn-} Extract all the data from the following filenames in a loop to build a session table. Include the original filename in its own column, to make finding the file later simpler:

fnames = ["242_CA1-d1=720,d2=1080.tif", "2045_CA3-d1=1080,d2=720.tif", "24_DG-d1=720,d2=720.tif", "52313_CA1-d1=720,d2=720.tif", "4_DG-d1=1080,d2=1080.tif"]

sessions = []

for fname in fnames:

    # Session ID
    start_idx = 0
    end_idx = fname.index('_')
    session_id = fname[start_idx:end_idx]
    
    # Height
    start_idx = fname.index("d1=") + len("d1=")
    end_idx = fname.index(",")
    height = int(fname[start_idx:end_idx])

    # Width
    start_idx = fname.index("d2=") + len("d2=")
    end_idx = fname.index(".")
    width = int(fname[start_idx:end_idx])

    # Brain Region
    start_idx = fname.index("_") + len("_")
    end_idx = fname.index('-')
    brain_region = fname[start_idx:end_idx]

    session = {"SessionID": session_id, "Height": height, "Width": width, "BrainRegion": brain_region, "Filename": fname}
    sessions.append(session)

df = pd.DataFrame(sessions)
df

Section 4: Variable-Length Data on Variable Keys: Using a Double-Separator to Store Keys Directly in the Filename

Introduction: Extracting the key-value pairs in a filename can be fully automated when they use a double-separator method. This technique is particularly useful when dealing with variable-length data and keys, a common scenario in scientific data management, including neuroscience research. By embedding key-value pairs in the filename itself, researchers can create self-descriptive files that contain crucial metadata in an organized and accessible format.

"sess=232_subj=Bill_grp=Control.txt"

In this method, filenames are constructed using two separators: one to separate different metadata elements (e.g., ‘_’) and another to distinguish between keys and their corresponding values (e.g., ‘=’). For example, in the filename above, each underscore separates different metadata items, and the equals sign distinguishes the key (e.g., ‘sess’, ‘subj’, ‘grp’) from its value.

Here, we’ll practice splitting these filenames to extract each key-value pair and store them in a Python dictionary. This practice is invaluable for organizing data in a way that is both human-readable and easily parsed programmatically, streamlining data analysis and retrieval.

Reference Table:

Exercises

Example: Extract all the data from the filename:

fname = "sess=232_subj=Bill_grp=Control.txt"

base, ext = fname.split('.')
data = {}
for item in base.split('_'):
    key, value = item.split('=')
    data[key] = value

data

{'sess': '232', 'subj': 'Bill', 'grp': 'Control'}

Exercise: Extract all the data from the filename

fname = "day-22 clinic-Tuebingen room-3.dat"

base, ext = fname.split('.')
data = {}
for item in base.split(' '):
    key, value = item.split('-')
    data[key] = value

data

{'day': '22', 'clinic': 'Tuebingen', 'room': '3'}

Exercise: Extract all the data from the following filenames in a loop to build a session table. Include the original filename in its own column, to make finding the file later simpler:

fnames = ["sessId-11_height-720_width-1028_region-DG.tif", "sessId-13_height-720_width-720.tif", "height-720_width-1028_region-DG_sessId-110.tif", "height-720_width-1028_region-DG_sessId-110_quality-bad.tif"]
fnames

['sessId-11_height-720_width-1028_region-DG.tif',
 'sessId-13_height-720_width-720.tif',
 'height-720_width-1028_region-DG_sessId-110.tif',
 'height-720_width-1028_region-DG_sessId-110_quality-bad.tif']

sessions = []

for fname in fnames:
    base, ext = fname.split('.')
    session = {}
    session["filename"] = fname
    for item in base.split('_'):
        key, value = item.split('-')
        session[key] = value
    
    sessions.append(session)

df = pd.DataFrame(sessions)
df

(Extra Demo) Making Data Model Contracts Explicit With Schemas

In scientific research, particularly in fields like neuroscience, it’s crucial to have a clear understanding of the data structure you’re working with. A schema, or a data model contract, serves as a blueprint for the data, outlining its format and the relationships between different data elements. By defining these contracts explicitly, you ensure that your data adheres to a specific structure, which facilitates more efficient and error-free data processing.

In this demonstration, we explore the use of Python’s built-in namedtuple feature from the collections module to create explicit schemas. A namedtuple allows you to create tuple-like objects that are accessible via named fields, making your code more self-documenting and easy to understand.

The example below illustrates how this would work:

from collections import namedtuple

# The Schema
MetadataModel = namedtuple("MetadataModel", "subject date group sess_num")

# Extracting the data
fname = "Arthur_20241008_control_1.txt"
base, ext = fname.split('.')

# Putting the data into the schema
data_tuple = MetadataModel(*base.split('_'))
data_tuple

MetadataModel(subject='Arthur', date='20241008', group='control', sess_num='1')

Named tuples can be converted to dictionaries using the _asdict() method.

data_dict = data_tuple._asdict()
data_dict

{'subject': 'Arthur', 'date': '20241008', 'group': 'control', 'sess_num': '1'}

Python comes with several built-in utilities for making these schemas: below is a reference comparing three of them. Very handy for writing well-documented code! Each of these is a way to create structured data types, but they have different features and use cases:

Each of these tools serves a different purpose:

• collections.namedtuple is great for when you need a simple, lightweight container with named fields.
• typing.NamedTuple is useful for a similar purpose but with the added benefit of type hints.
• dataclasses.dataclass is more suited for complex data structures where you might need mutability, default values, and built-in methods for common tasks.

Choosing the right tool depends on your specific needs, especially in terms of complexity, mutability, and the requirement for type hinting.