Looking at the README in that directory, that command is probably out dated. It should be as "easy" as running
make html in the docdirectory. Note however that by default it will run all of the gallery examples which can take a long time. There is a way to make it not do that but I don't remember off the top of my head
@djhoese Really I'm just looking for documentation to read and it seems sparse. I stumbled on that folder in the repo and thought I had to build the docs myself to get documentation I can learn from. Where would you say the best docs to learn vispy are? I also noticed multiple blogs and wiki's on the website are linked but the links are dead.
@sreenath1994s a lot of things have changed since then, so it's hard to tell what's the issue without some code. Can you reproduce the change with a small snippet of code?
# -*- coding: utf-8 -*-
# vispy: gallery 30
# -----------------------------------------------------------------------------
# Copyright (c) Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
"""
Draw a SurfacePlot
==================
This example demonstrates the use of the SurfacePlot visual.
"""
import sys
import numpy as np
from vispy import app, scene
from vispy.util.filter import gaussian_filter
canvas = scene.SceneCanvas(keys='interactive', bgcolor='w')
view = canvas.central_widget.add_view()
view.camera = scene.TurntableCamera(up='z', fov=60)
# Simple surface plot example
# x, y values are not specified, so assumed to be 0:50
z = np.random.normal(size=(250, 250), scale=200)
z[100, 100] += 50000
z = gaussian_filter(z, (10, 10))
p1 = scene.visuals.SurfacePlot(z=z, color=(0.3, 0.3, 1, 1), shading='smooth')
p1.transform = scene.transforms.MatrixTransform()
p1.transform.scale([1/249., 1/249., 1/249.])
p1.transform.translate([-0.5, -0.5, 0])
view.add(p1)
xax = scene.Axis(pos=[[-0.5, -0.5], [0.5, -0.5]], tick_direction=(0, -1),
font_size=16, axis_color='k', tick_color='k', text_color='k',
parent=view.scene)
xax.transform = scene.STTransform(translate=(0, 0, -0.2))
yax = scene.Axis(pos=[[-0.5, -0.5], [-0.5, 0.5]], tick_direction=(-1, 0),
font_size=16, axis_color='k', tick_color='k', text_color='k',
parent=view.scene)
yax.transform = scene.STTransform(translate=(0, 0, -0.2))
# Add a 3D axis to keep us oriented
axis = scene.visuals.XYZAxis(parent=view.scene)
if __name__ == '__main__':
canvas.show()
if sys.flags.interactive == 0:
app.run()
@sreenath1994s it seems that the crux of the issue is that the default light direction changed. Not sure why or when; you problem can be easily fixed by changing the light direction to something better. In the example above:
p1.shading_filter.light_dir = 1, 1, 1this should be close enought to what you had before. If not, play around with the number until you're happy :)
You can probably get what you want by playign around with the shading filter parameters
if you're in ipython, you can do
p1.shading_filter? and it will give you the documentation for it
you can change specular light, shininess and so on
for the example here: https://github.com/vispy/vispy/blob/main/examples/demo/gloo/realtime_signals.py
are the individual plots considered vertex and are the individual plot locations set by the 'index' variable on line 39? I would like to build on this example for a basic real time stream plotter from a USB link. Also, have you guys ever tested this with pyinstaller so it can be packaged up with a python application and deployed as an exe?
@m3atwad There are multiple issues discussing pyinstaller use. See vispy/vispy#1643 and vispy/vispy#2300
for that example and what you're doing, how many signals do you want to plot at once? I personally really dislike this example and want to rename it or remove it. It creates these many plots by "cheating" and doesn't resemble what a real world maintainable and flexible application would look like.
@djhoese thank you very much for you reply! I see, I'm ok with python but very new to the GPU situation and am trying to at most plot 4 different plots from an ADC. I am using matplot lib now and get a decent frame rate using blitting with ~2048 16 bit integers. I am trying to upgrade this to maybe 1M points of floats. I would at most just do 4 of these plots and am just nervous about investing a lot of my dev time in another platform that ultimately is a dead end. This approach looks awesome but I'm so new I don't know what I don't know yet to pull the trigger in commiting dev time to this library. Is it possible to elaborate on what this example is missing? Also, is the individual plot location and settings (not data points / y values but actual size, location etc...) set by the shader code (FRAG and VERT shader lines)?
@rougier I apologize if asking this many questions on here is inappropriate for this forum. Please let me know if so. My goal is to effectively create a vector of data and refresh a plot. In an ideal situation I'm trying to end up with a software window that mimics an oscilloscope to view electrical signals. So with this said I'm looking to get a decent framerate for a data vector and plot (1 to 4) that I can manipulate. What do you mean "continuously displayed on screen" and are you recommending I have a giant data vector I append to and just move the index for plotting rather than create a new data vector every time that I would submit to vispy?
For an oscilloscope-like display, you don't need much data since the resolution of the screen limits what you can display. Let's say you want to display 2000 vertices, you need a vertex buffer of 2000 floats but you need to update only one at each timestep (unless your data refresh rate is faster than the display refresh rate). The trick to update the vertex buffer is to have an index that is incremented by one at each time step adn this where you upload your new data. Now, the shader must be aware of this index to properly display the line, that is actually two lines. Best is to test using matplotlib. For example, let's pretend a numpy array is a vertex buffer holding y value only and plot it using a mpl.plot. You'll need to split it in two line plots I think.
so as an example right now I have 60 bytes coming in as a block rather than a byte data stream. This will yield 30 16bit values. So would I need to loop through all 30 and added them individually to the vertex buffer? It looks like this example is doing just what I'm referring to with a chunk of 10 values.
You can upload any contiguous chunk of data with a single call, no need to loop. In numpy, you would write something like
Z[index:index+chunk_size] = chunk, index += chunk_size But you have to take care of the case where index is close to the end of Z. in such case, you need to split your data in two parts and make 2 uploads.
Here is one implementation with glumpy: https://github.com/glumpy/glumpy/blob/master/examples/realtime-signals.py
@m3atwad The key in the glumpy example is the
np.roll. You could also do this kind of thing on a numpy array that's 2 times the size of the buffer you need and treat it like a circular buffer. You update the array in two places with your new chunk, but the data you upload to matplotlib/vispy is the slice of the large array that represents the current N data points.
The key point of this whole discussion is that you could use vispy or matplotlib for this, but since you're already using matplotlib with blitting you should be fine. If you reduce the resolution of your data (ex. average or only show some of the data) and/or reduce how often you try to update the display then you should see better results while not effecting what you/the user actually sees
Mmm I need to manually align a point cloud. I'm changing de cross-section over the Z axis of my cloud, then i can compare the shape in that section with a given ellipse to visually check if it is aligned or not. I.e. If the cloud is not aligned i'll see an offset between the cross-section and the ellipse. Then i need to rotate and store the data to work with the aligned cloud in the future
I'm not sure if I understand what you are doing and what you are having trouble with without some minimal example. You could even use fake point cloud (random) data. You didn't answer the question I had before though which may help. Are you using a camera? Are you using the SceneCanvas or the regular Canvas? You want to change the STTransform, but which one? The one on your visual or a camera?
mmm I'm using panzoom camera over a SceneCanvas. Now i can properly rotate the cloud translating > rotating > translating back. When I create my scatter = scene.visuals.Markers(), then scatter.set_data(pos=points) I would like to get back the points of my rotated cloud, where can I found all of these points when I add a transform component?
@rougier Hi Nicholas, quick question about glumpy. I am trying to run your linked example and I think I have everything installed but am stuck at the "back end" requirement. I have wxpython installed, but it seems thats not what is meant by installing one of the backends. What type of backend does python need to be installed to run these examples? Is there a difference between wxpython and Wx3?
I'm using this as a reference - https://glumpy.readthedocs.io/en/latest/installation.html