This module is designed to provide the basic analysis and visualization for a brief view of the dataset.
Dataset Viewer button in the
Console interface of QGrain, you will see a new window like above. Its main content is a table to display the statistic parameters and classification groups. Right-click the table region, a menu showing the available actions will be popped up.
After loading this dataset, you can see there are statistic parameters and classification groups in the table. You can resize the window or adjust the sliders to see more information. To keep it simple and efficient, all samples have been divided into several pages, each page shows 20 samples. By clicking the
Next button, you can jump to the previous or next page. You also can click the combo box to select a page directly. At below, there are two checkboxes to adjust the method which is used to calculate the statistic parameters. Another combo box is used to adjust the grades of proportions. Note: these three options not only affect the table view but also affect the output file of
Save Summary action. Yes, by clicking
Save Summary action, you can save the parameters and groups to an Excel file. You can click here to download it to have a look.
Many basic charts are built-in in this module, including cumulative curve chart, frequency curve chart, frequency 3D chart, Folk (1954)’s classification diagrams, and Blott & Pye (2012)’s classification diagrams.
By right-clicking the table, you can see these plotting actions. To be more flexible, each chart provides four sub-actions.
Plot Selected Samples: Clear previous samples in the chart, and plot selected samples.
Append Selected Samples: Append selected samples to the chart.
Plot All Samples: Clear previous samples in the chart, and plot all samples in the current dataset.
Append All Samples: Append all samples in the current dataset to the chart.
The charts are based on the well-known visualization module Matplotlib, and use the style module SciencePlot to refine its appearances. Matplotlib provides a navigation toolbar to adjust the figure, e.g., zoom figure; adjust figure layout; edit title, labels, and line styles. With this toolbar, the most common requirements could be satisfied. If you are not satisfied with it, you can output the data and use familiar tools to plot charts. In principle, all data in QGrain should be designed exportable, if you can not output what you need, feel free to contact the author. Most bitmap (e.g.,
*.jpg) and vectorgraph (
*.svg) formats are supported. It’s recommended to save it as an
svg file, and use other software (e.g., Adobe Illustrator) to edit and output other formats (e.g.,
*.tiff) for print and publication.
Here, we give an example to show that how to plot Blott & Pye (2012)’s SSC diagram of several Chinese loess datasets. The scatter points of the same datasets should have the same style, and the styles of different datasets should be different.
At first, we have tidied up the GSDs of different profiles into one Excel file under the sample data layout.
And then, we loaded one sheet (i.e., the GSDs of one profile), right-clicked the table, and select the
Append All Samplessub-action to plot all samples of this profile. We used the same method to process other datasets. Finally, the Blott & Pye (2012)’s SSC diagram looks like below.
Editbutton in the toolbar, clicked the
Curvestab in the popped dialog, there were the curves in the figure. Edit the styles of the last several lines. Finally, the Blott & Pye (2012)’s SSC diagram looks like below.
Save the figure.