kivy garden Grap xmin xmax update (real time) - graph

is there a way to update xmin and xmax of Graph in kivy garden?
At the moment, my xmin is 0 and xmax is 10 and I am plotting a list containin (x,y) points. y starts from 0 and is incremented every second and x is just a counter that counts iterations. so once x is > 10 the graph is not updated in real time so the x axis does not move like e.g. 1-11 and next second 2-12 and next 3-13 etc etc so I always show 10 values but the grap is 'live'.
How to achieve it in kivy garden Graph?
graph = Graph(
xlabel='Iteration',
ylabel='Value',
x_ticks_minor=1,
x_ticks_major=5,
y_ticks_major=1,
y_grid_label=True,
x_grid_label=True,
padding=5,
xlog=False,
ylog=False,
x_grid=True,
y_grid=True,
xmin=0,
xmax=10,
ymin=0,
ymax=11,
**graph_theme)

Never used this flower, but try graph.xmin = <something>. If it doesn't work, the code probably has other name for that variable, because xmin in Graph(...) is only keyword argument. Try to look at its __init__() and after you see what has the value, try to access it via graph.<variable>. If it's not a local variable, then it should work.
However, this only says how to change it manually. If you have more of your code except graph = Graph(...), paste it, an issue may be there. Otherwise you could try to report a glitch at garden's repo for Graph.

According to the code found in "garden.graph" library you can call the instance of the graph to set properties of xmax, xmin, ymax and ymin.
As an example, I am seting the property of ymax to be maximum value of my data set:
Class Test(Widget):
data = #list of datapoints
graph_test = ObjectProperty(None)
def setgraph:
plot = MeshLinePlot(color=[1, 0, 0, 1])
self.graph_test.ymax=max(data[1])
plot.points = [(data[0][i], data[1][i]) for i in range(0,500)]
self.graph_test.add_plot(plot)
I also have a .kv file that contains the following
#:kivy 1.0.9
<Test>:
graph_test : graph_test
Graph:
id: graph_test
plot: MeshLinePlot
xlabel:'X'
ylabel:'Y'
x_ticks_minor:1000000000000
x_tics_major:5000000000000
y_ticks_major:1000
y_grid_label:True
x_grid_label:True
padding:5
x_grid:True
y_grid:True
xmin:123413241234
xmax:123412341234
ymin:2000
ymax:15000
pos: 0, 0
size: root.width , root.height

Old question, but I'm doing this with some Kivy Graphs and rather than resetting the XMin and XMax (which means those numbers would keep counting up for as long as the graph is running "live"), we shift the data points and leave the X range alone.
We built this simple LiveGraph class to wrap it, works nicely and is relatively performant.
class LiveGraph(Graph):
def __init__(self, num_data_points=50, **kwargs):
super(LiveGraph, self).__init__(**kwargs)
self._num_data_points = num_data_points
self.xmin = 0
self.xmax = num_data_points
build_chart()
def build_chart(self):
plot = MeshLinePlot()
# Initialize with empty Y values to start with
plot.points = [(x,0) for x in range(self._num_data_points)
# I tested a number of ways to do this - this ended up being
# the fastest.
def add_point(self, point_value):
new_points = self.plots[0].points
new_points = [(i, new_points[i+1][1]) for i in range(self._num_data_points - 1)]
new_points.append([len(new_points) - 1, new_value])
self.plots[0].points = new_points

Related

Bokeh Colorbar Vertical title to right of colorbar?

I'm trying to do something that I'd normally consider trivial but seems to be very difficult in bokeh: Adding a vertical colorbar to a plot and then having the title of the colorbar (a.k.a. the variable behind the colormapping) appear to one side of the colorbar but rotated 90 degrees clockwise from horizontal.
From what I can tell of the bokeh ColorBar() interface (looking at both documentation and using the python interpreter's help() function for this element), this is not possible. In desperation I have added my own Label()-based annotation. This works but is klunky and displays odd behavior when deployed in a bokeh serve situation--that the width of the data window on the plot varies inversely with the length of the title colorbar's title string.
Below I've included a modified version of the bokeh server mpg example. Apologies for its complexity, but I felt this was the best way to illustrate the problem using infrastructure/data that ships with bokeh. For those unfamiliar with bokeh serve, this code snippet needs to saved to a file named main.py that resides in a directory--for the sake of argument let's say CrossFilter2--and in the parent directory of CrossFilter2 one needs to invoke the command
bokeh serve --show CrossFilter2
this will then display in a browser window (localhost:5006/CrossFilter2) and if you play with the color selection widget you will see what I mean, namely that short variable names such as 'hp' or 'mpg' result in a wider data display windows than longer variable names such as 'accel' or 'weight'. I suspect that there may be a bug in how label elements are sized--that their x and y dimensions are swapped--and that bokeh has not understood that the label element has been rotated.
My questions are:
Must I really have to go to this kind of trouble to get a simple colorbar label feature that I can get with little-to-no trouble in matplotlib/plotly?
If I must go through the hassle you can see in my sample code, is there some other way I can do this that avoids the data window width problem?
import numpy as np
import pandas as pd
from bokeh.layouts import row, widgetbox
from bokeh.models import Select
from bokeh.models import HoverTool, ColorBar, LinearColorMapper, Label
from bokeh.palettes import Spectral5
from bokeh.plotting import curdoc, figure, ColumnDataSource
from bokeh.sampledata.autompg import autompg_clean as df
df = df.copy()
SIZES = list(range(6, 22, 3))
COLORS = Spectral5
# data cleanup
df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)
columns = sorted(df.columns)
discrete = [x for x in columns if df[x].dtype == object]
continuous = [x for x in columns if x not in discrete]
quantileable = [x for x in continuous if len(df[x].unique()) > 20]
def create_figure():
xs = df[x.value].tolist()
ys = df[y.value].tolist()
x_title = x.value.title()
y_title = y.value.title()
name = df['name'].tolist()
kw = dict()
if x.value in discrete:
kw['x_range'] = sorted(set(xs))
if y.value in discrete:
kw['y_range'] = sorted(set(ys))
kw['title'] = "%s vs %s" % (y_title, x_title)
p = figure(plot_height=600, plot_width=800,
tools='pan,box_zoom,wheel_zoom,lasso_select,reset,save',
toolbar_location='above', **kw)
p.xaxis.axis_label = x_title
p.yaxis.axis_label = y_title
if x.value in discrete:
p.xaxis.major_label_orientation = pd.np.pi / 4
if size.value != 'None':
groups = pd.qcut(df[size.value].values, len(SIZES))
sz = [SIZES[xx] for xx in groups.codes]
else:
sz = [9] * len(xs)
if color.value != 'None':
coloring = df[color.value].tolist()
cv_95 = np.percentile(np.asarray(coloring), 95)
mapper = LinearColorMapper(palette=Spectral5,
low=cv_min, high=cv_95)
mapper.low_color = 'blue'
mapper.high_color = 'red'
add_color_bar = True
ninety_degrees = pd.np.pi / 2.
color_bar = ColorBar(color_mapper=mapper, title='',
#title=color.value.title(),
title_text_font_style='bold',
title_text_font_size='20px',
title_text_align='center',
orientation='vertical',
major_label_text_font_size='16px',
major_label_text_font_style='bold',
label_standoff=8,
major_tick_line_color='black',
major_tick_line_width=3,
major_tick_in=12,
location=(0,0))
else:
c = ['#31AADE'] * len(xs)
add_color_bar = False
if add_color_bar:
source = ColumnDataSource(data=dict(x=xs, y=ys,
c=coloring, size=sz, name=name))
else:
source = ColumnDataSource(data=dict(x=xs, y=ys, color=c,
size=sz, name=name))
if add_color_bar:
p.circle('x', 'y', fill_color={'field': 'c',
'transform': mapper},
line_color=None, size='size', source=source)
else:
p.circle('x', 'y', color='color', size='size', source=source)
p.add_tools(HoverTool(tooltips=[('x', '#x'), ('y', '#y'),
('desc', '#name')]))
if add_color_bar:
color_bar_label = Label(text=color.value.title(),
angle=ninety_degrees,
text_color='black',
text_font_style='bold',
text_font_size='20px',
x=25, y=300,
x_units='screen', y_units='screen')
p.add_layout(color_bar, 'right')
p.add_layout(color_bar_label, 'right')
return p
def update(attr, old, new):
layout.children[1] = create_figure()
x = Select(title='X-Axis', value='mpg', options=columns)
x.on_change('value', update)
y = Select(title='Y-Axis', value='hp', options=columns)
y.on_change('value', update)
size = Select(title='Size', value='None',
options=['None'] + quantileable)
size.on_change('value', update)
color = Select(title='Color', value='None',
options=['None'] + quantileable)
color.on_change('value', update)
controls = widgetbox([x, y, color, size], width=200)
layout = row(controls, create_figure())
curdoc().add_root(layout)
curdoc().title = "Crossfilter"
You can add a vertical label to the Colorbar by plotting it on a separate axis and adding a title to this axis. To illustrate this, here's a modified version of Bokeh's standard Colorbar example (found here):
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.models import LogColorMapper, LogTicker, ColorBar
from bokeh.layouts import row
plot_height = 500
plot_width = 500
color_bar_height = plot_height + 11
color_bar_width = 180
output_file('color_bar.html')
def normal2d(X, Y, sigx=1.0, sigy=1.0, mux=0.0, muy=0.0):
z = (X-mux)**2 / sigx**2 + (Y-muy)**2 / sigy**2
return np.exp(-z/2) / (2 * np.pi * sigx * sigy)
X, Y = np.mgrid[-3:3:100j, -2:2:100j]
Z = normal2d(X, Y, 0.1, 0.2, 1.0, 1.0) + 0.1*normal2d(X, Y, 1.0, 1.0)
image = Z * 1e6
color_mapper = LogColorMapper(palette="Viridis256", low=1, high=1e7)
plot = figure(x_range=(0,1), y_range=(0,1), toolbar_location=None,
width=plot_width, height=plot_height)
plot.image(image=[image], color_mapper=color_mapper,
dh=[1.0], dw=[1.0], x=[0], y=[0])
Now, to make the Colorbar, create a separate dummy plot, add the Colorbar to the dummy plot and place it next to the main plot. Add the Colorbar label as the title of the dummy plot and center it appropriately.
color_bar = ColorBar(color_mapper=color_mapper, ticker=LogTicker(),
label_standoff=12, border_line_color=None, location=(0,0))
color_bar_plot = figure(title="My color bar title", title_location="right",
height=color_bar_height, width=color_bar_width,
toolbar_location=None, min_border=0,
outline_line_color=None)
color_bar_plot.add_layout(color_bar, 'right')
color_bar_plot.title.align="center"
color_bar_plot.title.text_font_size = '12pt'
layout = row(plot, color_bar_plot)
show(layout)
This gives the following output image:
One thing to look out for is that color_bar_width is set wide enough to incorporate both the Colorbar, its axes labels and the Colorbar label. If the width is set too small, you will get an error and the plot won't render.
As of Bokeh 0.12.10 there is no built in label available for colorbars. In addition to your approach or something like it, another possibility would be a custom extension, though that is similarly not trivial.
Offhand, a colobar label certainly seems like a reasonable thing to consider. Regarding the notion that it ought to be trivially available, if you polled all users about what they consider should be trivially available, there will be thousands of different suggestions for what to prioritize. As is very often the case in the OSS world, there are far more possible things to do, than there are people to do them (less than 3 in this case). So, would first suggest a GitHub Issue to request the feature, and second, if you have the ability, volunteering to help implement it. Your contribution would be valuable and appreciated by many.

Is it correct value of cluster center is refer to a*b (LAB color space) value

I am working on image processing and I need to segment colour in image into several groups.I am using k-means algorithm to do that.This time, I want to give English name to the colour which already been clustering. this is my coding:
cform = makecform('srgb2lab');
lab = applycform(a,cform);
ab = double(lab(:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);
nColors=6;
[cluster_idx, cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', 'Replicates',3);
pixel_labels = reshape(cluster_idx,nrows,ncols);
segmented_images = cell(1,3);
rgb_label = repmat(pixel_labels,[1 1 3]);
as far as I understand, cluster_center is refer to a*b (lab colour space) value. Is it correct? If so, why I get the value of cluster_center more than range value already set (-128 to 128 for lab color space).

ImageView.setImage axes parameter does not switch X-Y dimensions

I have modified the ImageView example by adding the statement data[:, ::10, :] = 0, which sets every tenth element of the middle dimension to 0. The program now shows horizontal lines. This is consistent with the documentation of the ImageView.setImage function: the default axes dictionary is {'t':0, 'x':1, 'y':2, 'c':3}. However, when I change this to {'t':0, 'x':2, 'y':1, 'c':3}, nothing changes where I would expect to get vertical rows.
So my question is: how can I give the row dimension a higher precedence in PyQtGraph? Of course I can transpose all my arrays myself before passing them to the setImage function but I prefer not to. Especially since both Numpy and Qt use the row/column convention and not X before Y. I don't see why PyQtGraph chooses the latter.
For completeness, find my modified ImageView example below.
import numpy as np
from pyqtgraph.Qt import QtCore, QtGui
import pyqtgraph as pg
app = QtGui.QApplication([])
## Create window with ImageView widget
win = QtGui.QMainWindow()
win.resize(800,800)
imv = pg.ImageView()
win.setCentralWidget(imv)
win.show()
win.setWindowTitle('pyqtgraph example: ImageView')
## Create random 3D data set with noisy signals
img = pg.gaussianFilter(np.random.normal(size=(200, 200)), (5, 5)) * 20 + 100
img = img[np.newaxis,:,:]
decay = np.exp(-np.linspace(0,0.3,100))[:,np.newaxis,np.newaxis]
data = np.random.normal(size=(100, 200, 200))
data += img * decay
data += 2
## Add time-varying signal
sig = np.zeros(data.shape[0])
sig[30:] += np.exp(-np.linspace(1,10, 70))
sig[40:] += np.exp(-np.linspace(1,10, 60))
sig[70:] += np.exp(-np.linspace(1,10, 30))
sig = sig[:,np.newaxis,np.newaxis] * 3
data[:,50:60,50:60] += sig
data[:, ::10, :] = 0 # Make image a-symmetrical
## Display the data and assign each frame a time value from 1.0 to 3.0
imv.setImage(data, xvals=np.linspace(1., 3., data.shape[0]),
axes={'t':0, 'x':2, 'y':1, 'c':3}) # doesn't help
## Start Qt event loop unless running in interactive mode.
if __name__ == '__main__':
import sys
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
Looking through ImageView.py, setImage() parses the axes dictionary and based on presence of 't' it builds the z-axis/frame slider, and that's it. Rearranging the axes seems unimplemented yet.

PyQtGraph sliding window acquires y offset

I'm trying to create an scrolling plot window with PyQtGraph. The plot itself has multiple axes. I used this as the basis of the multiple axes. I used this one as the base to do the scrolling bit.
My problem is that for my data, the scrolling plot seems to acquire an y-offset, increasing as time goes on. I also tried using the same data to display in an accumulating plot (though I really would rather to do a scrolling view) and it didn't acquire any y-offset.
This is what it looks at the end of my test sample - missing the effect of y-offset gradually increasing -
Of course, I would like for the y-offset not to appear, the top plot should be identical to the last 50 samples of the bottom plot
Both plots have identical data sets.
The code that I'm using to generate this is:
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtGui
import numpy as np
from time import sleep
win = pg.GraphicsWindow()
win.setWindowTitle('Sliding Window Test')
p1 = win.addPlot()
p1.setLabels(left='Large Range')
## create third ViewBox.
## this time we need to create a new axis as well.
p3 = pg.ViewBox()
ax3 = pg.AxisItem('right')
p1.layout.addItem(ax3, 2, 3)
p1.scene().addItem(p3)
ax3.linkToView(p3)
p3.setXLink(p1)
ax3.setZValue(-10000)
ax3.setLabel('Small Range', color='#ff0000')
win.nextRow()
p5 = win.addPlot()
p5.setLabels(left='Large Range')
## create third ViewBox.
## this time we need to create a new axis as well.
p7 = pg.ViewBox()
ax7 = pg.AxisItem('right')
p5.layout.addItem(ax7, 2, 3)
p5.scene().addItem(p7)
ax7.linkToView(p7)
p7.setXLink(p5)
ax7.setZValue(-10000)
ax7.setLabel('Small Range', color='#ff0000')
## Handle view resizing
def updateViews():
## view has resized; update auxiliary views to match
global p1, p3, p5, p7
p3.setGeometry(p1.vb.sceneBoundingRect())
p7.setGeometry(p5.vb.sceneBoundingRect())
## need to re-update linked axes since this was called
## incorrectly while views had different shapes.
## (probably this should be handled in ViewBox.resizeEvent)
p3.linkedViewChanged(p1.vb, p3.XAxis)
p7.linkedViewChanged(p5.vb, p7.XAxis)
updateViews()
p1.vb.sigResized.connect(updateViews)
p5.vb.sigResized.connect(updateViews)
data1 = []
data3 = []
curve1 = p1.plot()
curve3 = pg.PlotCurveItem(pen='r')
p3.addItem(curve3)
curve5 = p5.plot()
curve7 = pg.PlotCurveItem(pen='r')
p7.addItem(curve7)
data1 = [1000.0*r - 400 for r in np.random.random(size=600)]
data3 = [1.5*r for r in np.random.random(size=600)]
p1.setRange(yRange=(-400, 600))
p3.setRange(yRange=(0, 1.5))
p5.setRange(yRange=(-400, 600))
p7.setRange(yRange=(0, 1.5))
timer = pg.QtCore.QTimer()
r = 0
def update():
global timer
global r
if r > 50:
curve1.setData(data1[r-50:r])
curve3.setData(data3[r-50:r])
curve1.setPos(r - 50, r)
curve3.setPos(r - 50, r)
else:
curve1.setData(data1[:r])
curve3.setData(data3[:r])
curve5.setData(data1[:r])
curve7.setData(data3[:r])
r +=1
if r >= 600:
timer.stop()
timer.timeout.connect(update)
timer.start(100)
## Start Qt event loop unless running in interactive mode or using pyside.
if __name__ == '__main__':
import sys
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
Simple error: both curves in the top plot are shifted in the y-direction because of these lines:
curve1.setPos(r - 50, r)
curve3.setPos(r - 50, r)
They should instead look like:
curve1.setPos(r - 50, 0)
curve3.setPos(r - 50, 0)

Combaning Multiple plots into a Single plot

I am writing a code to generate four stimulations and then generate graphs. My code works, but I want instead of generating four graphs I want to combine them all in one graph. How can I do that?
My code:
queueSimulation <- function(arriverate, servrate, endtime) {
queue = numeric(0)
arrivetimes = rexp(10000, arriverate)
servtimes = rexp(10000, servrate)
clock = 0.0
clist=c()
qlist=c()
while(clock <= endtime) {
if(length(queue) > 0 && queue[1] < arrivetimes[1]) {
clock = clock + queue[1]
queue = queue[-1]
}
else {
clock = clock + arrivetimes[1]
queue[length(queue) + 1] = servtimes[1]
arrivetimes = arrivetimes[-1]
servtimes = servtimes[-1]
}
#queue_size= length(round(clock, 2))
clist = c(clist , clock)
qlist = c(qlist , length(queue))
}
a<-data.frame(time=clist , qsize=qlist)
print(a)
mean1<-mean(qlist)
cat("Average :", mean1, "\n")
plot(a)
}
and calling the function:
queueSimulation(1.0, 5.0, 100)
queueSimulation(2.0, 4.0, 100)
queueSimulation(2.3, 3.5, 100)
queueSimulation(4.0, 5.0, 100)
There might be a better solution to this, but how about slightly changing your approach.
1- In your function, add two variables, one for color (cl) and one to tell your function if your plotting the main plot or just adding lines (pl). 1 for main and 0 for lines.
function(arriverate, servrate, endtime,cl,pl) {
2- call your plot with an if statement, and fix your y axis to range from 0 to 200.
if(pl==1){plot(a,col=cl,ylim=c(0,200),type="l")} else{lines(a,col=cl)}}
and then, call your function with theses two variables (cl and pl) :
queueSimulation(1.0, 5.0, 100,"red",1)
queueSimulation(2.0, 4.0, 100,"blue",0)
queueSimulation(2.3, 3.5, 100,"green",0)
queueSimulation(4.0, 5.0, 100,"black",0)
The problem I see with this is that your simulations can get values way over 200 for the y axis, maybe try to find a way to get max y values in one of your call.
Take a look at layout, specifically put layout(matrix(1:4,nrow=2)) (or a variant) before you call your plotting functions.

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