Open AccessPosted Content10.5194/SED-6-3141-2014

GrainSizeTools: a Python script for estimating the dynamically recrystallized grain size from grain sectional areas

- 01 Nov 2014

- Vol. 6, Iss: 2, pp 3141-3196

TL;DR: In this article, the authors present a survey of the literature in this area: https://www.referred.org.au/blog/blogging-and-blogging/

Abstract: Introduction Conclusions References

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1. What are the contributions mentioned in the paper "An evaluation of different measures of dynamically recrystallized grain size for paleopiezometry or paleowattometry studies" ?

A free, open-source, easyto-handle script named GrainSizeTools ( https: //sourceforge. net/projects/grainsizetools/ ) was developed with the aim of facilitating the adoption of this measure of grain size in paleopiezometry or paleowattometry studies.

2. What are the future works mentioned in the paper "An evaluation of different measures of dynamically recrystallized grain size for paleopiezometry or paleowattometry studies" ?

This could be useful in the future for comparative purposes if a method capable of measuring directly the actual 3-D grain size distribution becomes mainstream.. The consequence is that no fully reliable correlation can be established among them since the introduction of errors change across studies.

Figure 4. The Gaussian kernel density estimator is a function that stacks a Gaussian “bell curve” on top of each measurement and whose standard deviation, determined by the local probability density, defines the bandwidth (the equivalent to the histogram’s bin size/width).

Table 1. Steps for obtaining the cross-sectional areas of the grains using the ImageJ application.

Figure 11. Estimated actual 3-D grain size distribution from sample MAL-05 performed with CSD Corrections (left) and StripStar (right). To draw the lines, the middle values of each bin estimated by the applications were used, taking into account different number of classes. The frequency peak grain size and other statistical parameters of interest were calculated using all the middle values of the modal intervals obtained. The inset (on the right) is the curve estimated by StripStar considering 20 classes but using on the vertical axis a linear scale instead of logarithmic. Note that the actual 3-D grain size curve shows similarities to a log-normal grain size distribution.

Figure 5. Grain size distribution plots of a bimodal discrete population consisting of a 60 / 40 % mixture of spherical particles with sizes 100 and 120 respectively. The contribution in volume is 46.5 / 53.5 % respectively. The two local maxima in both plots indicate the existence of two populations of spheres with sizes specified by the local maxima. Both plots show different modal intervals due to the different approaches (see Sect. 2.2). The area-weighted plot locates the modal interval in the population that represents the major/main phase (i.e. the volumetric contribution).

Figure 7. Results of the Monte Carlo simulations to find the number of grains necessary to achieve reproducibility using a natural dynamically recrystallized mylonite (sample MAL-05) and the mean grain size. (a) Evolution of the mean and the standard deviation during the increase of sample size. Note that the mean barely changes its value due to the large number of runs (5000) used in each sample size while the standard deviation declined monotonically as we increased the number of grains. (b) Evolution of the coefficient of variation (σ /mean) using the sigma-2 and sigma-3 values during the increasing of sample size. In this particular simulation, the plot shows that when 428 (for 2-sigma) or 953 (for 3-sigma) grains are measured, the coefficient of variation of the mean grain size obtained is below 4 %. The inset in (b) shows a linear scale. (c, d) Minimum number of grains obtained by repeating the previous simulation 250 times and using the 2-sigma and 3-sigma values to estimate the coefficient of variation respectively. Pdf means probability density function.

Figure 10. Testing reproducibility of GrainSizeTools script using different sub-data sets from the natural sample MAL-05.

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GrainSizeTools: a Python script for estimating the dynamically recrystallized grain size from grain sectional areas

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Most frequently asked questions

1. What are the contributions mentioned in the paper "An evaluation of different measures of dynamically recrystallized grain size for paleopiezometry or paleowattometry studies" ?

2. What are the future works mentioned in the paper "An evaluation of different measures of dynamically recrystallized grain size for paleopiezometry or paleowattometry studies" ?

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Citations

Using Free/Libre and Open Source Software in the Geological Sciences

Mapping nanoscale carrier confinement in polycrystalline graphene by terahertz spectroscopy

Mapping nanoscale carrier confinement in polycrystalline graphene by terahertz spectroscopy

References

NIH Image to ImageJ: 25 years of image analysis

Matplotlib: A 2D Graphics Environment

Density estimation for statistics and data analysis

Density Estimation for Statistics and Data Analysis

Multivariate Density Estimation, Theory, Practice and Visualization

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