![]() ![]() For illustration, the code below would change raster of value. Sometimes, a special number indicates missing value in a raster (such as -999 or any obvious value that will be outside the range of the normal dataset you are working with). Just use the missing value NA to replace the 0. Therefore you will have to manually copy the legend names from the Web site and manually create an informative legend, but you can automatically display the codes for the colors. It will be relatively easy to change a cell value. Consequently, the figure caption ends up being long-winded, procedural, and not at all interesting. Fortunately, it is easy to make the caption succinct and descriptive with a few quick adjustments to the figure. The colors are stored with the TIFF file, but either the legend class names are not in there or else they are not read by raster. The problem is not that people are excluding the information, rather they are putting everything in the figure caption. ![]() But I’ve come across far too many figures breaking one or more of these rules. All pretty straightforward, so you would think any figure published in a scientific journal would adhere to this as a minimum. Alternatively, JupyterLab may work better for you.)Ĭontinue reading “Organise scripts and figures easily with Jupyter Notebooks” Author Ken Hughes Posted on Categories Figures, Matlab, Python, Software Tags collapsible headings, Jupyter, jupyterlab, matplotlib, notebook, Python, Software, themes 1 Comment on Organise scripts and figures easily with Jupyter Notebooks To make a good schematic, copy, adapt, and refineīefore leaving high school, every scientist should have learned all the things a graph should contain: a descriptive title, labels for every axis, appropriately spaced tick marks, and a legend if necessary. Rather than trying to remember what file I want, I need only remember which figure I want. (I say archive because I much prefer to do the bulk of my exploratory analysis in an editor. For example, I use a single notebook to archive the code for all figures in a paper and, more importantly, I can associate each set of code with the figure it generates. Regardless of whether its Python, R, Julia, Matlab, or pretty much any other type of code, Jupyter Notebooks solve the problem. Names like ISW_plume_plots.m, new_ISW_model_plots.m, and plot_model_behaviour.m. ![]() Names that probably meant something to me at the time, but are hardly descriptive months or years later. Before realising that Jupyter Notebooks could solve most of my problems, I would have directories with dozens of scripts with filenames of varying levels of ambiguity. Keeping track of scripts used to generate figures is difficult. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |