filepattern 2.2.3.post1.dev0

Utilities for parsing files in a directory based on a file name pattern.

Filepattern

The filepattern utility is used to store files that follow a pattern, where the pattern is analogous to a simplified regular expression. The need for filepattern arises in situations where large amounts of data with a systematic naming convention needs to be filtered by patterns in the naming. For example, one may have a directory containing segmented images where the name contains information such as the channel, the column value, and the row value. filepattern provides the ability to extract all images containing such a naming pattern, filter by the row or column value, or group files by one or more of the aforementioned variables.

Summary

• Read the Docs
• Install
• Authors
• License
• Acknowledgments

Install

filepattern is both pip and conda installable by running pip install filepattern or conda install filepattern -c conda-forge

Build and Install

Alternatively, filepattern can either be build inside a conda environment or independently outside of it directly from the source.

Inside Conda

filepattern uses a CMake build system. Below is an example of how to build filepattern Python package inside a conda environment on Linux.

git clone https://github.com/PolusAI/filepattern.git
cd filepattern
conda install -y -c conda-forge compilers --file ci-utils/envs/conda_cpp.txt --file ci-utils/envs/conda_py.txt
CMAKE_ARGS="-DCMAKE_PREFIX_PATH=$CONDA_PREFIX -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX " python -m pip install . -vv

Without Using Conda

To build filepattern outside of a conda environment, use the following example.

git clone https://github.com/PolusAI/filepattern.git
cd filepattern
mkdir build_dep
cd build_dep
bash ../ci-utils/install_prereq_linux.sh
cd ..
export FILEPATTERN_DEP_DIR=./build_dep/local_install
python -m pip install . -vv

C++ Library

filepattern also comes with a C++ API. To build and install filepattern as a C++ library, following the steps below.

git clone https://github.com/PolusAI/filepattern.git
cd filepattern
mkdir build
cd build
bash ../ci-utils/install_prereq_linux.sh
cmake -Dfilepattern_SHARED_LIB=ON -DCMAKE_PREFIX_PATH=./local_install -DCMAKE_INSTALL_PREFIX=./local_install ../src/filepattern/cpp/
make -j4
make install

To link filepattern with the client code, use the following CMake statements.

find_package(filepattern REQUIRED)
target_link_libraries(client_executable PRIVATE filepattern::filepattern)

Java API

filepattern also supplies a Java API. To add filepattern as a dependency to a project, the following can be added to the pom.xml of the maven project.

<dependencies>
    <dependency>
        <groupId>ai.polus.utils</groupId>
        <artifactId>filepattern</artifactId>
        <version>LATEST</version>
    </dependency>
</dependencies>

The Java API can also be built from source using Maven. To build the project, run

git clone https://github.com/PolusAI/filepattern.git
cd filepattern
mvn clean install

To build a jar instead of installing filepattern, mvn clean package can be used in place of mvn clean install.

For more information of the Java API, see the Java API documentation

Filepattern

When only a path to a directory and a pattern are supplied to the constructor of filepattern, filepattern will iterate over the directory, matching the filenames in the directory to the filepattern. The filepattern can either be supplied by the user or can be found using the infer_pattern method of filepattern. For example, consider a directory containing the following files,

img_r001_c001_DAPI.tif
img_r001_c001_TXREAD.tif
img_r001_c001_GFP.tif

In each of these filenames, there are three descriptors of the image: the row, the column, and the channel. To match these files, the pattern img_r{r:ddd}_c{c:ddd}_{channel:c+} can be used. In this pattern, the named groups are contained within the curly brackets, where the variable name is before the colon and the value is after the colon. For the value, the descriptors d and c are used, which represent a digit and a character, respectively. In the example pattern, three d's are used to capture three digits. The + after c denotes that one or more characters will be captured, which is equivalent to [a-zA-z]+ in a regular expression. The + symbol may be used after either d or c.

To have filepattern guess what the pattern is for a directory, the static method infer_pattern can be used:

import filepattern as fp

path = 'path/to/directory'

pattern = fp.infer_pattern(path)

print(pattern)

The result is:

img_r001_c001_{r:c+}.tif

Note that the infer_pattern can also guess the patterns from stitching vectors and text files when a path to a text file is passed, rather than a path to a directory.

To retrieve files from a directory that match the filepattern, an iterator is called on the FilePattern object, as shown below. A user specified custom pattern, such as the one below, or the guessed pattern can be passed to the constructor.

import filepattern as fp
import pprint

filepath = "path/to/directory"

pattern = "img_r{r:ddd}_c{c:ddd}_{channel:c+}.tif"

files = fp.FilePattern(filepath, pattern)

for file in files():
    pprint.pprint(file)

The output is:

({'c': 1, 'channel': 'DAPI', 'r': 1},
 ['path/to/directory/img_r001_c001_DAPI.tif'])
({'c': 1, 'channel': 'TXREAD', 'r': 1},
 ['path/to/directory/img_r001_c001_TXREAD.tif'])
({'c': 1, 'channel': 'GFP', 'r': 1},
 ['path/to/directory/img_r001_c001_GFP.tif'])

As shown in this example, the output is a tuple where the first member is a map between the group name supplied in the pattern and the value of the group for each file name. The second member of the tuple is a vector containing the path to the matched file. The second member is stored in a vector for the case where a directory is supplied with multiple subdirectories. In this case, a third optional parameter can be passed to the constructor. If the parameter recursive is set to True, a recursive directory iterator will be used, which iterates over all subdirectories. If the basename of two files from two different subdirectories match, filepattern will add the path of the file to the vector in the existing tuple rather than creating a new tuple.

For example, consider the directory with the structure

/root_directory
    /DAPI
        img_r001_c001.tif
    /GFP
        img_r001_c001.tif
    /TXREAD
        img_r001_c001.tif

In this case, the subdirectories are split by the channel. Recursive matching can be used as shown below.

import filepattern as fp
import pprint

filepath = "path/to/root/directory"

pattern = "img_r{r:ddd}_c{c:ddd}.tif"

files = fp.FilePattern(filepath, pattern, recursive=True)

for file in files():
    pprint.pprint(file)

The output of this case is:

({'c': 1, 'r': 1},
 ['path/to/root/directory/DAPI/img_r001_c001.tif',
  'path/to/root/directory/GFP/img_r001_c001.tif',
  'path/to/root/directory/TXREAD/img_r001_c001.tif'])

Floating Point Support

`filepattern` has the ability to capture floating point values in file patterns. For example, if we have a set of files

img_r0.05_c1.15.tif
img_r1.05_c2.25.tif
img_r2.05_c3.35.tif

We can capture the values in a couple of different ways. Similar to capturing digits, the character f can be used to capture an element of a floating point number. Note that with this method, the decimal point in the number must be captured by an f. For example, in the file img_r0.05_c1.15.tif, the floating point numbers would be capture with ffff. The code to utilize this method is

filepath = "path/to/directory"

pattern = "img_r{r:ffff}_c{c:ffff}.tif"

files = fp.FilePattern(filepath, pattern)

for file in files():
    pprint.pprint(file)

The result is:

({'c': 1.15, 'r': 0.05},
 ['path/to/directory/img_r0.05_c1.15.tif'])
({'c': 2.25, 'r': 1.05},
 ['path/to/directory/img_r1.05_c2.25.tif'])
({'c': 3.35, 'r': 2.05},
 ['path/to/directory/img_r2.05_c3.35.tif'])

To capture floating point numbers with an arbitrary number of digits, we can use f+. This method operates in the same way as using d+ or c+, where all digits (and the decimal point) will be captured for a floating point of any length. The code for this method is

filepath = "path/to/directory"

pattern = "img_r{r:f+}_c{c:f+}.tif"

files = fp.FilePattern(filepath, pattern)

for file in files():
    pprint.pprint(file)

The result of this code is the same as the previous example.

The final method for capturing floating points is to use d to capture the digits and to add the decimal point where needed. For example, in the file img_r0.05_c1.15.tif, the floating point numbers could be captured using d.dd. The code for this method is:

filepath = "path/to/directory"

pattern = "img_r{r:d.dd}_c{c:d.dd}.tif"

files = fp.FilePattern(filepath, pattern)

for file in files():
    pprint.pprint(file)

Once again, the results are the same as the first example.

Note that d can be used to specify even more specific floating points. For example, if we want to capturing all floating points with one digit in the whole part and an arbitrary number of digits in the decimal, we can add d.d+ for the pattern. Similarly, this could be used in a reverse manner to capture an arbitrary number of digits in the whole part using d+.ddd.

Group By

If images need to be processed in a specific order, for example by the row number, the group_by function is used. With the directory

img_r001_c001_DAPI.tif
img_r002_c001_DAPI.tif
img_r001_c001_TXREAD.tif
img_r002_c001_TXREAD.tif
img_r001_c001_GFP.tif
img_r002_c001_GFP.tif

the images can be returned in groups where r is held constant by passing the parameter group_by='r' to the object iterator.

import filepattern as fp
import pprint

filepath = "path/to/directory"

pattern = "img_r{r:ddd}_c{c:ddd}_{channel:c+}.tif"

files = fp.FilePattern(filepath, pattern)

for file in files(group_by='r'):
    pprint.pprint(file)

The output is:

('r': 1, [({'c': 1, 'channel': 'DAPI', 'file': 0, 'r': 1},
  ['/home/ec2-user/Dev/FilePattern/data/example/img_r001_c001_DAPI.tif']),
 ({'c': 1, 'channel': 'TXREAD', 'file': 0, 'r': 1},
  ['/home/ec2-user/Dev/FilePattern/data/example/img_r001_c001_TXREAD.tif']),
 ({'c': 1, 'channel': 'GFP', 'file': 0, 'r': 1},
  ['/home/ec2-user/Dev/FilePattern/data/example/img_r001_c001_GFP.tif'])])
('r': 2, [({'c': 1, 'channel': 'DAPI', 'file': 0, 'r': 2},
  ['/home/ec2-user/Dev/FilePattern/data/example/img_r002_c001_DAPI.tif']),
 ({'c': 1, 'channel': 'GFP', 'file': 0, 'r': 2},
  ['/home/ec2-user/Dev/FilePattern/data/example/img_r002_c001_GFP.tif']),
 ({'c': 1, 'channel': 'TXREAD', 'file': 0, 'r': 2},
  ['/home/ec2-user/Dev/FilePattern/data/example/img_r002_c001_TXREAD.tif'])])

Note that the return of each call is a tuple where the first member is the group_by variable mapped to the current value and the second member is a list of files where the group_by variable matches the current value.

Get Matching

To get files where the variable matches a value, the get_matching method is used. For example, if only files from the TXREAD channel are needed, get_matching(channel=['TXREAD'] is called.

filepath = "/home/ec2-user/Dev/FilePattern/data/example"

pattern = "img_r{r:ddd}_c{c:ddd}_{channel:c+}.tif"

files = fp.FilePattern(filepath, pattern)

matching = files.get_matching(channel=['TXREAD'])

pprint.pprint(matching)

The output is:

[({'c': 1, 'channel': 'TXREAD', 'r': 1},
  ['/home/ec2-user/Dev/FilePattern/data/example/img_r001_c001_TXREAD.tif']),
 ({'c': 1, 'channel': 'TXREAD', 'r': 2},
  ['/home/ec2-user/Dev/FilePattern/data/example/img_r002_c001_TXREAD.tif'])]

Text files

filepattern can also take in a text file as an input rather than a directory. To use this functionality, a path to a text file is supplied to the path variable rather than a directory. When a text file is passed as input, each line of the text file will be matched to the pattern. For example, a text file containing containing the strings

img_r001_c001_DAPI.tif
img_r001_c001_TXREAD.tif
img_r001_c001_GFP.tif

can be matched to the pattern img_r{r:ddd}_c{c:ddd}_{channel:c+}.tif with:

import filepattern as fp
import pprint

filepath = "path/to/file.txt"

pattern = "img_r{r:ddd}_c{c:ddd}_{channel:c+}.tif"

files = fp.FilePattern(filepath, pattern)

for file in files():
    pprint.pprint(file)

The output is:

({'c': 1, 'channel': 'DAPI', 'r': 1},
 ['img_r001_c001_DAPI.tif'])
({'c': 1, 'channel': 'TXREAD', 'r': 1},
 ['img_r001_c001_TXREAD.tif'])
({'c': 1, 'channel': 'GFP', 'r': 1},
 ['img_r001_c001_GFP.tif'])

After calling filepattern on a text file, the group_by and get_matching functionality can be used the same as outlined in the FilePattern section.

Stitching vectors

filepattern can also take in stitching vectors as input. In this case, a path to a text file containing a stitching vector is passed to the path variable. A stitching vector has the following form,

file: x01_y01_wx0_wy0_c1.ome.tif; corr: 0; position: (0, 0); grid: (0, 0);
file: x02_y01_wx0_wy0_c1.ome.tif; corr: 0; position: (3496, 0); grid: (3, 0);
file: x03_y01_wx0_wy0_c1.ome.tif; corr: 0; position: (6992, 0); grid: (6, 0);
file: x04_y01_wx0_wy0_c1.ome.tif; corr: 0; position: (10488, 0); grid: (9, 0);

This stitching vector can be processed using

import filepattern as fp

filepath = 'path/to/stitching/vector.txt'

pattern = 'x0{x:d}_y01_wx0_wy0_c1.ome.tif'

files = fp.FilePattern(filepath, pattern)

for file in files():
    pprint.pprint(files)

The output is:

({'correlation': 0, 'gridX': 0, 'gridY': 0, 'posX': 0, 'posY': 0, 'x': 1},
 ['x01_y01_wx0_wy0_c1.ome.tif'])
({'correlation': 0, 'gridX': 3, 'gridY': 0, 'posX': 3496, 'posY': 0, 'x': 2},
 ['x02_y01_wx0_wy0_c1.ome.tif'])
({'correlation': 0, 'gridX': 6, 'gridY': 0, 'posX': 6992, 'posY': 0, 'x': 3},
 ['x03_y01_wx0_wy0_c1.ome.tif'])
({'correlation': 0, 'gridX': 9, 'gridY': 0, 'posX': 10488, 'posY': 0, 'x': 4},
 ['x04_y01_wx0_wy0_c1.ome.tif'])

As shown in the output, filepattern not only captures the specified variables from the pattern, but also captures the variables supplied in the stitching vector.

Out of core

filepattern has the ability to use external memory when the dataset is too large to fit in main memory, i.e. it utilizes disk memory along with RAM. It has the same functionality as filepattern, however it takes in an addition parameter called block_size, which limits the amount of main memory used by filepattern. Consider a directory containing the files:

img_r001_c001_DAPI.tif
img_r001_c001_TXREAD.tif
img_r001_c001_GFP.tif

This directory can be processed with only one file in memory as:

import filepattern as fp
import pprint

filepath = "path/to/directory"

pattern = "img_r{r:ddd}_c{c:ddd}_{channel:c+}.tif"

files = fp.FilePattern(filepath, pattern, block_size="125 B")


for file in files():
    pprint.pprint(file)

The output from this example is:

({'c': 1, 'channel': 'DAPI', 'r': 1},
 ['/home/ec2-user/Dev/FilePattern/data/example/img_r001_c001_DAPI.tif'])
({'c': 1, 'channel': 'TXREAD', 'r': 1},
 ['/home/ec2-user/Dev/FilePattern/data/example/img_r001_c001_TXREAD.tif'])
({'c': 1, 'channel': 'GFP', 'r': 1},
 ['/home/ec2-user/Dev/FilePattern/data/example/img_r001_c001_GFP.tif'])

Note that the block_size argument is provided in bytes (B) in this example, but also has the options for kilobytes (KB), megabytes (MB), and gigabytes (GB). The block_size must be under 1000 GB.

Group by and get matching

The out of core version of filepattern contains the same functionalities as the in memory version. group_by is called the same way, i.e.,

for file in files(group_by="r"):
    pprint.pprint(file)

The output remains identical to the in memory version.

The get_matching functionality remains the same, however the API is slightly different. In this case, get_matching is called as

for matching in files.get_matching(channel=['TXREAD'])
    pprint.pprint(matching)

where the output is returned in blocks of block_size. The output is:

({'c': 1, 'channel': 'TXREAD', 'r': 1},
 ['/home/ec2-user/Dev/FilePattern/data/example/img_r001_c001_TXREAD.tif'])

Out of Core: text files and stitching vectors

Out of core processing can also be used for stitching vectors and text files. To utilize this functionality, call filepattern the same way as described previously, but add in the block_size parameter, as described in the (Out of Core)[#out-of-core] section.

Contributing

We welcome contributions to filepattern! Please see CONTRIBUTING.md for detailed guidelines.

Quick Start for Contributors

1. Fork the repository
2. Create a feature branch: git checkout -b feat/my-feature
3. Make your changes following Conventional Commits
4. Push to your fork and submit a pull request

Commit Message Format

We use Conventional Commits for automated releases:

• feat: - New features (minor version bump)
• fix: - Bug fixes (patch version bump)
• docs: - Documentation changes
• refactor: - Code refactoring
• test: - Test additions or changes
• chore: - Maintenance tasks

Example: feat: add support for nested directory patterns

For breaking changes, use feat!: or include BREAKING CHANGE: in the commit footer (major version bump).

Release Process

Releases are fully automated:

1. Commits following conventional format are pushed to master
2. Release Please creates/updates a Release PR with version bump and changelog
3. When the Release PR is merged, a GitHub Release is created
4. Automated workflows publish to PyPI and Maven Central

You don't need to manually update version numbers - the automation handles this based on your commit messages!

Authors

Jesse McKinzie([e-mail], [e-mail]) Nick Schaub ([e-mail], [e-mail])

License

This project is licensed under the MIT License Creative Commons License - see the LICENSE file for details

Acknowledgments

• This utility was inspired by the notation found in the MIST algorithm developed at NIST.