shareloc-utils 0.1.4

Utility tools for ShareLoc.XYZ

ShareLoc Utilities

Utility tools for ShareLoc.XYZ in Python

Features

• Batch Downloading datasets from https://shareloc.xyz website
• Parser for *.smlm files

Installation

pip install -U shareloc-utils

Usage for batch downloading

The easiest way to download the dataset is to go to https://shareloc.xyz and click the download icon on the dataset.

You will then get a generated command which you can use for downloading the dataset.

If you want to know how the generated commands works, here we will give a few examples.

Basically, you can pass datasets url directly to the command line like this:

python -m shareloc_utils.batch_download --datasets=https://sandbox.zenodo.org/record/891810 --output_dir=./output

If you want to download multiple datasets, use comma to separate multiple dataset URL or just Zenodo IDs.

python -m shareloc_utils.batch_download --datasets=891810,887832 --sandbox --output_dir=./output

Note that when you use Zenodo ID, you will need an additional parameter --sandbox if you are using the sandbox server.

If you want to convert the downloaded dataset to text file format (e.g. CSV), you can add --conversion after the command.

To print all other options, type:

python -m shareloc_utils.batch_download --help

Downloading multiple datasets

The ShareLoc.XYZ website support bookmark feature (similar to a shopping cart) which you can mark multiple datasets and download them all.

To use it, you move your mouse on top of the dataset card you are interested in, then click the bookmark icon to mark it. Repeat the process and mark all the datasets you want to download. Then click the bookmark icon in the navigation bar and click "Download All".

Use the SMLM file parser

In Python, you use the read_smlm_file function to parse the *.smlm file downloaded from ShareLoc(https://shareloc.xyz).

In the following example, we first parse the tables in the smlm file, then generate a histogram image:

from PIL import Image
from shareloc_utils.smlm_file import read_smlm_file, plot_histogram

# parse the .smlm file
manifest = read_smlm_file("./localization_table.smlm")
# one file can contain multiple localization tables
tables = manifest["files"]

# generate an histogram image for the first table
histogram = plot_histogram(tables[0]["data"], value_range=(0, 255))

# save the histogram image as 16-bit png file
im = Image.fromarray(histogram.astype("uint16"))
im.save("output.png")

Development

• Install and set up development environment.

  pip install -r requirements_dev.txt
  

  This will install all requirements. It will also install this package in development mode, so that code changes are applied immediately without reinstall necessary.

• Here's a list of development tools we use.

  • black
  • flake8
  • mypy
  • pydocstyle
  • pylint
  • pytest
  • tox

• It's recommended to use the corresponding code formatter and linters also in your code editor to get instant feedback. A popular editor that can do this is vscode.

• Run all tests, check formatting and linting.

  tox
  

• Run a single tox environment.

  tox -e lint
  

• Reinstall all tox environments.

  tox -r
  

• Run pytest and all tests.

  pytest
  

• Run pytest and calculate coverage for the package.

  pytest --cov-report term-missing --cov=shareloc_utils
  

• Continuous integration is by default supported via GitHub actions. GitHub actions is free for public repositories and comes with 2000 free Ubuntu build minutes per month for private repositories.