Plots | A graph plotting app for GNOME | Math library
kandi X-RAY | Plots Summary
kandi X-RAY | Plots Summary
Plots is a graph plotting app for GNOME. Plots makes it easy to visualise mathematical formulae. In addition to basic arithmetic operations, it supports trigonometric, hyperbolic, exponential and logarithmic functions, as well as arbitrary sums and products. Plots is designed to integrate well with the GNOME desktop and takes advantage of modern hardware using OpenGL, and currently supports OpenGL 3.3+.
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Top functions reviewed by kandi - BETA
- Activate the plot
- Add a new formula
- Update the zoom reset child
- Add command to history
- Handle keypress
- Return the ancestors of the given elementlist
- Cancel selection
- Calculate the current selection position
- Called when the paste is finished
- Handles mouse press events
- Called when a key is pressed
- Function to register export button
- Render the image
- Dissolves the list
- Load configuration
- Undo the formula
- Compute the metrics for the current stack
- Called when a motion is received
- Dissolves the current element
- Backspace the cursor
- Render the canvas
- Render an area
- Generate a GlSL string
- Greedy insert
- Compute metrics
- Draws the current element
Plots Key Features
Plots Examples and Code Snippets
Community Discussions
Trending Discussions on Plots
QUESTION
I have source (src
) image(s) I wish to align to a destination (dst
) image using an Affine Transformation whilst retaining the full extent of both images during alignment (even the non-overlapping areas).
I am already able to calculate the Affine Transformation rotation and offset matrix, which I feed to scipy.ndimage.interpolate.affine_transform
to recover the dst
-aligned src
image.
The problem is that, when the images are not fuly overlapping, the resultant image is cropped to only the common footprint of the two images. What I need is the full extent of both images, placed on the same pixel coordinate system. This question is almost a duplicate of this one - and the excellent answer and repository there provides this functionality for OpenCV transformations. I unfortunately need this for scipy
's implementation.
Much too late, after repeatedly hitting a brick wall trying to translate the above question's answer to scipy
, I came across this issue and subsequently followed to this question. The latter question did give some insight into the wonderful world of scipy
's affine transformation, but I have as yet been unable to crack my particular needs.
The transformations from src
to dst
can have translations and rotation. I can get translations only working (an example is shown below) and I can get rotations only working (largely hacking around the below and taking inspiration from the use of the reshape
argument in scipy.ndimage.interpolation.rotate
). However, I am getting thoroughly lost combining the two. I have tried to calculate what should be the correct offset
(see this question's answers again), but I can't get it working in all scenarios.
Translation-only working example of padded affine transformation, which follows largely this repo, explained in this answer:
...ANSWER
Answered 2022-Mar-22 at 16:44If you have two images that are similar (or the same) and you want to align them, you can do it using both functions rotate and shift :
QUESTION
I made the following 25 network graphs (all of these graphs are copies for simplicity - in reality, they will all be different):
...ANSWER
Answered 2022-Mar-03 at 21:12While my solution isn't exactly what you describe under Option 2
, it is close. We use combineWidgets()
to create a grid with a single column and a row height where one graph covers most of the screen height. We squeeze in a link between each widget instance that scrolls the browser window down to show the following graph when clicked.
Let me know if this is working for you. It should be possible to automatically adjust the row size according to the browser window size. Currently, this depends on the browser window height being around 1000px.
I modified your code for the graph creation slightly and wrapped it in a function. This allows us to create 25 different-looking graphs easily. This way testing the resulting HTML file is more fun! What follows the function definition is the code to create a list
of HTML objects that we then feed into combineWidgets()
.
QUESTION
i am currently working with plotly i have a function called plotChart that takes a dataframe as input and plots a candlestick chart. I am trying to figure out a way to pass a list of dataframes to the function plotChart and use a plotly dropdown menu to show the options on the input list by the stock name. The drop down menu will have the list of dataframe and when an option is clicked on it will update the figure in plotly is there away to do this. below is the code i have to plot a single dataframe
...ANSWER
Answered 2022-Feb-18 at 07:18I adapted an example from the plotly community to your example and created the code. The point of creation is to create the data for each subplot and then switch between them by means of buttons. The sample data is created using representative companies of US stocks. one issue is that the title is set but not displayed. We are currently investigating this issue.
QUESTION
There are so many ways to define colour scales within ggplot2
. After just loading ggplot2
I count 22
functions beginging with scale_color_*
(or scale_colour_*
) and same number beginging with scale_fill_*
. Is it possible to briefly name the purpose of the functions below? Particularly I struggle with the differences of some of the functions and when to use them.
- scale_*_binned()
- scale_*_brewer()
- scale_*_continuous()
- scale_*_date()
- scale_*_datetime()
- scale_*_discrete()
- scale_*_distiller()
- scale_*_fermenter()
- scale_*_gradient()
- scale_*_gradient2()
- scale_*_gradientn()
- scale_*_grey()
- scale_*_hue()
- scale_*_identity()
- scale_*_manual()
- scale_*_ordinal()
- scale_*_steps()
- scale_*_steps2()
- scale_*_stepsn()
- scale_*_viridis_b()
- scale_*_viridis_c()
- scale_*_viridis_d()
What I tried
I've tried to make some research on the web but the more I read the more I get onfused. To drop some random example: "The default scale for continuous fill scales is scale_fill_continuous()
which in turn defaults to scale_fill_gradient()
". I do not get what the difference of both functions is. Again, this is just an example. Same is true for scale_color_binned()
and scale_color_discrete()
where I can not name the difference. And in case of scale_color_date()
and scale_color_datetime()
the destription says "scale_*_gradient
creates a two colour gradient (low-high), scale_*_gradient2
creates a diverging colour gradient (low-mid-high), scale_*_gradientn
creates a n-colour gradient." which is nice to know but how is this related to scale_color_date()
and scale_color_datetime()
? Looking for those functions on the web does not give me very informative sources either. Reading on this topic gets also chaotic because there are tons of color palettes in different packages which are sequential/ diverging/ qualitative plus one can set same color in different ways, i.e. by color name, rgb, number, hex code or palette name. In part this is not directly related to the question about the 2*22
functions but in some cases it is because providing a "wrong" palette results in an error (e.g. the error"Continuous value supplied to discrete scale
).
Why I ask this
I need to do many plots for my work and I am supposed to provide some function that returns all kind of plots. The plots are supposed to have similiar layout so that they fit well together. One aspect I need to consider here is that the colour scales of the plots go well together. See here for example, where so many different kind of plots have same colour scale. I was hoping I could use some general function which provides a colour palette to any data, regardless of whether the data is continuous or categorical, whether it is a fill or col easthetic. But since this is not how colour scales are defined in ggplot2
I need to understand what all those functions are good for.
ANSWER
Answered 2022-Feb-01 at 18:14This is a good question... and I would have hoped there would be a practical guide somewhere. One could question if SO would be a good place to ask this question, but regardless, here's my attempt to summarize the various scale_color_*()
and scale_fill_*()
functions built into ggplot2
. Here, we'll describe the range of functions using scale_color_*()
; however, the same general rules will apply for scale_fill_*()
functions.
There are 22 functions in all, but happily we can group them intelligently based on practical usage scenarios. There are three key criteria that can be used to define practically how to use each of the scale_color_*()
functions:
Nature of the mapping data. Is the data mapped to the color aesthetic discrete or continuous? CONTINUOUS data is something that can be explained via real numbers: time, temperature, lengths - these are all continuous because even if your observations are
1
and2
, there can exist something that would have a theoretical value of1.5
. DISCRETE data is just the opposite: you cannot express this data via real numbers. Take, for example, if your observations were:"Model A"
and"Model B"
. There is no obvious way to express something in-between those two. As such, you can only represent these as single colors or numbers.The Colorspace. The color palette used to draw onto the plot. By default,
ggplot2
uses (I believe) a color palette based on evenly-spaced hue values. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces.The level of Specification. Generally, once you have defined if the scale function is continuous and in what colorspace, you have variation on the level of control or specification the user will need or can specify. A good example of this is the functions:
*_continuous()
,*_gradient()
,*_gradient2()
, and*_gradientn()
.
We can start off with continuous scales. These functions are all used when applied to observations that are continuous variables (see above). The functions here can further be defined if they are either binned or not binned. "Binning" is just a way of grouping ranges of a continuous variable to all be assigned to a particular color. You'll notice the effect of "binning" is to change the legend keys from a "colorbar" to a "steps" legend.
The continuous example (colorbar legend):
QUESTION
Is there a way to put text along a density line, or for that matter, any path, in ggplot2? By that, I mean either once as a label, in this style of xkcd: 1835, 1950 (middle panel), 1392, or 2234 (middle panel). Alternatively, is there a way to have the line be repeating text, such as this xkcd #930 ? My apologies for all the xkcd, I'm not sure what these styles are called, and it's the only place I can think of that I've seen this before to differentiate areas in this way.
Note: I'm not talking about the hand-drawn xkcd style, nor putting flat labels at the top
I know I can place a straight/flat piece of text, such as via annotate
or geom_text
, but I'm curious about bending such text so it appears to be along the curve of the data.
I'm also curious if there is a name for this style of text-along-line?
Example ggplot2 graph using annotate(...)
:
Above example graph modified with curved text in Inkscape:
Edit: Here's the data for the first two trial runs in March and April, as requested:
...ANSWER
Answered 2021-Nov-08 at 11:31Great question. I have often thought about this. I don't know of any packages that allow it natively, but it's not terribly difficult to do it yourself, since geom_text
accepts angle
as an aesthetic mapping.
Say we have the following plot:
QUESTION
I've built this new ggplot2
geom layer I'm calling geom_triangles
(see https://github.com/ctesta01/ggtriangles/) that plots isosceles triangles given aesthetics including x, y, z
where z
is the height of the triangle and
the base of the isosceles triangle has midpoint (x,y) on the graph.
What I want is for the geom_triangles()
layer to automatically provide legend components for the height and width of the triangles, but I am not sure how to do that.
I understand based on this reference that I may need to adjust the draw_key
argument in the ggproto
StatTriangles
object, but I'm not sure how I would do that and can't seem to find examples online of how to do it. I've been looking at the source code in ggplot2
for the draw_key
functions, but I'm not sure how I would introduce multiple legend components (one for each of height and width) in a single draw_key
argument in the StatTriangles
ggproto
.
ANSWER
Answered 2022-Jan-30 at 18:08I think you might be slightly overcomplicating things. Ideally, you'd just want a single key drawing method for the whole layer. However, because you're using a Stat
to do the majority of calculations, this becomes hairy to implement. In my answer, I'm avoiding this.
Let's say I'd want to use a geom-only implementation of such a layer. I can make the following (simplified) class/constructor pair. Below, I haven't bothered width_scale
or height_scale
parameters, just for simplicity.
QUESTION
In a R Notebook there is a function that makes many plots and print summary statistics in the console. I would like to get the plot and the console output (i.e. summary statistics) side by side on the HTML output.
Here is a very simple example:
...ANSWER
Answered 2022-Jan-18 at 17:43For the example setup, I would recommend splitting up the operations to easily fit them side-by-side using pandoc syntax for multiple columns. In this way, we can just call the specifics we want.
QUESTION
Python 3.9 on Mac running OS 11.6.1. My application involves placing a plot on a frame inside my root window, and I'm struggling to get the plot to take up a larger portion of the window. I thought rcParams
in matplotlib.pyplot
would take care of this, but I must be overlooking something.
Here's what I have so far:
...ANSWER
Answered 2022-Jan-14 at 23:23try something like this:
QUESTION
I want to create a clickable histogram in shiny
but I don't know if it is possible.
Some months ago I saw a clickable volcano plot which gives you a table of what you click.
Source: https://2-bitbio.com/2017/12/clickable-volcano-plots-in-shiny.html
The closest post that I found about creating clickable histograms is this one Click to get coordinates from multiple histogram in shiny
However, I don't want to get the coordinates. I want the rownames of the dataframe.
Having this dataframe, can I get the rownames everytime I click a bar from the histogram?
...ANSWER
Answered 2021-Nov-27 at 18:49This is a great question, and what makes it challenging is that the qplot/ggplot charts are static images. The below app.r
is an example of how I would do it. I'd love to see other approaches.
In essence:
- Create a sequence of numbers that will be used both as the breaks in your histogram and as intervals in your dataframe. I based these on user inputs, but you could hardcode them.
- Assign a "bin" value to each row in the dataframe based on the interval in which the value falls.
- Record the x-coordinate from the user's click event and assign that a "bin" value based on the same set of intervals.
- Subset your dataframe and retain only those records where the "bin" value of the data matches the "bin" value of the x-coordinate from the user's click event.
Otherwise, if you're willing to go the d3 route, you could explore something like this posted by R Views.
QUESTION
I have an RMarkdown document which summarizes how many records (rows) have various attributes by group. I would like to be able to manipulate which records are included in the table by filtering before the summarizing. I've created a minimal but similar mockup below.
What I would like is an interactive checkbox that would effectively "comment or uncomment" out the line
...ANSWER
Answered 2021-Dec-15 at 22:58Try adding a JS aggregate function callback, instead of using the built-in aggregation:
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You can use Plots like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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