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Recent Releases of dice

dice - v1.2.0

What's Changed

  • Comprehensive refactoring and modernization of the DICE codebase: by @thiebes in https://github.com/thiebes/DICE/pull/19

- Python
Published by thiebes 10 months ago

dice - v1.1.4

Main Application Changes

Improved CNR estimates

This release addresses overestimation of noise power in the FFT-based CNR estimation function, fft_cnr. When performing a real-valued Fast Fourier Transform (FFT) using rfft, the output is a single-sided spectrum. Each frequency component in this spectrum represents the combined power from the original signal's positive and negative frequencies.

Minor changes

The peak normalization and root-mean-squared calculation in the fft_cnr function were also simplified in this commit.

Acknowledgments

  • Signed-off-by: Joseph J. Thiebes joseph@thiebes.org

- Python
Published by thiebes almost 2 years ago

dice - v1.1.3

Main Application Changes

Bug Fixes

  • Fixed invalid escape sequence warnings in dice.py by using raw string literals.

Minor Changes

  • Minor updates and changes to README.md for better clarity.
  • Added citation file CITATION.cff.

CNR Estimate Web Application Changes

UI Improvements:

  • Updated formatting to respond to browser width.
  • Other minor formatting changes.

Refactoring:

  • Refactored the web application to use Jinja2 templates for improved organization and readability.
  • Created separate templates for the home page, result page, and error page.

File Structure Updates:

  • Created a static directory for images and other files.
  • Created a templates directory for html templates.

New Files:

  • Created styles.css file.
  • Added routes to serve robots.txt and sitemap.xml.
  • Created sitemap.xml for better search engine indexing.
  • Created robots.txt file.
  • Created site.webmanifest.
  • Added favicon files for the web app.
  • Created README.md for the web app.
  • Updated dicewebapp.py to include new icon files and site manifest.

Acknowledgments

  • Signed-off-by: Joseph J. Thiebes joseph@thiebes.org

- Python
Published by thiebes almost 2 years ago

dice - v1.1.2 (v1.1.0 update formatting corrected)

This is identical to v1.1.0 but with corrected formatting to the update. This is the second attempt to correct the formatting; unfortunately Zenodo does not have a way to preview, and updates are permanent. Corrected update follows:

News

Updates in version 1.1.0

The following changes have been included in this version of DICE:

  • A requirements.txt file has been added to facilitate installation.
  • Contrast-to-noise ratio (CNR) estimates are now rounded to two decimal places, as there is no reason to believe the CNR estimates will be more accurate than that.
  • A web app for calculating contrast-to-noise estimates for 1D noisy Gaussian profiles has been added to the repository. The web app is being served here.
  • A new gradient colormap, dice_gradient, has been added to colordefs().
    • dice_gradient is a colorblind-accessible, gradient color map that transitions from light lavender to dark forest green, optimized for clarity in both color and grayscale printing. This gradient is generated with cubehelix2,3.
    • dice-grad
  • A new section on interpreting the results of simulations has been added to the README.md file.
  • Citation of the paper has been added in the documentation.
  • The README.md file has been reorganized and updated.

References

  1. Joseph J. Thiebes, Erik M. Grumstrup; Quantifying noise effects in optical measures of excited state transport. J. Chem. Phys. 28 March 2024; 160 (12): 124201. doi:10.1063/5.0190347.
  2. Green, D. A.; A colour scheme for the display of astronomical intensity images. Bulletin of the Astronomical Society of India 2011; 39: 289–295. doi:10.48550/arXiv.1108.5083.
  3. Davenport, J. R. Cubehelix, or How I Learned to Love Black & White Printers. If We Assume. Retrieved March 15, 2024, from https://ifweassume.blogspot.com/2013/05/cubehelix-or-how-i-learned-to-love.html.

- Python
Published by thiebes about 2 years ago

dice - v1.1.1 (v1.1.0 update formatting corrected)

This is identical to v1.1.0 but with corrected formatting to the update. Corrected update follows:

News

Updates in version 1.1.0

The following changes have been included in this version of DICE:

  • A requirements.txt file has been added to facilitate installation.
  • Contrast-to-noise ratio (CNR) estimates are now rounded to two decimal places, as there is no reason to believe the CNR estimates will be more accurate than that.
  • A web app for calculating contrast-to-noise estimates for 1D noisy Gaussian profiles has been added to the repository. The web app is being served here.
  • A new gradient colormap, dice_gradient, has been added to colordefs().
    • dice_gradient is a colorblind-accessible, gradient color map that transitions from light lavender to dark forest green, optimized for clarity in both color and grayscale printing. This gradient is generated with cubehelix2,3.
    • dice-grad

  • A new section on interpreting the results of simulations has been added to the README.md file.
  • Citation of the paper has been added in the documentation.
  • The README.md file has been reorganized and updated.

References

[1] Joseph J. Thiebes, Erik M. Grumstrup; Quantifying noise effects in optical measures of excited state transport. J. Chem. Phys. 28 March 2024; 160 (12): 124201. doi:10.1063/5.0190347.
[2] Green, D. A.; A colour scheme for the display of astronomical intensity images. Bulletin of the Astronomical Society of India 2011; 39: 289–295. doi:10.48550/arXiv.1108.5083.
[3] Davenport, J. R. Cubehelix, or How I Learned to Love Black & White Printers. If We Assume. Retrieved March 15, 2024, from https://ifweassume.blogspot.com/2013/05/cubehelix-or-how-i-learned-to-love.html.

- Python
Published by thiebes about 2 years ago

dice - v1.1.0

News

Updates in version 1.1.0

The following changes have been included in this version of DICE:

  • A requirements.txt file has been added to facilitate installation.
  • Contrast-to-noise ratio (CNR) estimates are now rounded to two decimal places, as there is no reason to believe the CNR estimates will be more accurate than that.
  • A web app for calculating contrast-to-noise estimates for 1D noisy Gaussian profiles has been added to the repository. The web app is being served here.
  • A new gradient colormap, dice_gradient, has been added to colordefs().
    • dice_gradient is a colorblind-accessible, gradient color map that transitions from light lavender to dark forest green, optimized for clarity in both color and grayscale printing. This gradient is generated with cubehelix2,3.
    • dice-grad

  • A new section on interpreting the results of simulations has been added to the README.md file.
  • Citation of the paper has been added in the documentation.
  • The README.md file has been reorganized and updated.

References

[1] Joseph J. Thiebes, Erik M. Grumstrup; Quantifying noise effects in optical measures of excited state transport. J. Chem. Phys. 28 March 2024; 160 (12): 124201. doi:10.1063/5.0190347.
[2] Green, D. A.; A colour scheme for the display of astronomical intensity images. Bulletin of the Astronomical Society of India 2011; 39: 289–295. doi:10.48550/arXiv.1108.5083.
[3] Davenport, J. R. Cubehelix, or How I Learned to Love Black & White Printers. If We Assume. Retrieved March 15, 2024, from https://ifweassume.blogspot.com/2013/05/cubehelix-or-how-i-learned-to-love.html.

- Python
Published by thiebes about 2 years ago

dice - DICE Launch

Introduction:

We are excited to announce the first official release of DICE (Diffusion Insight Computation Engine), v1.0.0 - "DICE Launch". DICE is an open-source tool designed for researchers in time-resolved microscopy and related fields. It evaluates the precision and accuracy of diffusion coefficient estimates derived from optical measures of excited state transport, offering a robust method for assessing experimental accuracy and precision by simulating parameters that mirror your experimental setup.

Key Features:

  • Simulation of time-series profiles: DICE simulates a population of excited states as a Gaussian distribution profile undergoing decay and diffusion.
  • Noise addition and Gaussian fit: Incorporates white noise into profiles and fits the time-evolved noisy profiles with Gaussian functions.
  • Linear fit and diffusion coefficient estimation: Uses a weighted least squares method to fit Mean Squared Displacement (MSD) values to a linear function, estimating the diffusion coefficient.
  • Statistical analysis and presentation: Provides a statistical view of the precision and accuracy of diffusion estimates and a customizable plotting function for result presentation.

Getting Started:

  • Installation: Ensure you have Python installed, then run pip install numpy pandas matplotlib scipy statsmodels joblib to install the necessary packages.
  • Basic usage: Modify the parameters.txt file to align with your experimental parameters, then run the simulation using the run_dice.py script.

Documentation:

For detailed instructions and information, please refer to our README.

- Python
Published by thiebes over 2 years ago