I am using pyqtgraph to plot some data and noticed that when I move the plot from my laptop screen to a second monitor, the scaling on the plot is affected:
laptop monitor:
external monitor:
notice that the axes got "compressed", and the plot is no longer scaled properly on the second monitor.
I found others reporting similar issues on the web, but could not find any real solution. One solution suggested was to make the monitors' resolutions the same. I don't like this solution because I'd have to sacrifice laptop resolution to accommodate my lower resolution external monitor.
The other solution I found was to add the line app.setAttribute(QtCore.Qt.AA_Use96Dpi) to the main loop, prior to instantiating the Qapplication as shown below, to allegedly have Qt ignore the OS's DPI settings:
def main():
import sys
app = QtWidgets.QApplication(sys.argv)
app.setAttribute(QtCore.Qt.AA_Use96Dpi)
MainWindow =GraphWindow()
MainWindow.show()
sys.exit(app.exec_())
This seems at first to work, because the plotted data is scaled properly on the axes. However, it doesn't seem to really work -- the addition of this line affected the scaling of the axes on the laptop as shown below (same data is now plotted on axes that span 0 to 7000 on the x-axis, and -2 to -26dB on the Yaxis):
,
but did "fix" the issue when moving the plot onto the second monitor to look like the first "original" laptop plot shown above.
This is particularly worrisome, because in the case of the laptop output after the app.setAttribute(QtCore.Qt.AA_Use96Dpi) instruction "looks" right, but misrepresents the actual data. I could have easily missed this had included this instruction when I first plotted the data.
What is the right way to have the plot accurately display regardless of the OS's DPI setting and monitor resolutions? It is very strange that the plotted data seems disassociated with the axis values.
Here is a mininimal reproducible sample:
from PyQt5 import QtWidgets, QtCore
from pyqtgraph import PlotWidget, plot
import pyqtgraph as pg
import sys # We need sys so that we can pass argv to QApplication
import os
from numpy.random import seed
from numpy.random import randint
class MainWindow(QtWidgets.QMainWindow):
def __init__(self, *args, **kwargs):
super(MainWindow, self).__init__(*args, **kwargs)
self.graphWidget = pg.PlotWidget()
self.setCentralWidget(self.graphWidget)
x = [1,2,3,4,5,6,7,8,9,10]
seed(1)
y = randint(5,35,10)
# plot data: x, y values
self.graphWidget.plot(x, y)
def main():
app = QtWidgets.QApplication(sys.argv)
app.setAttribute(QtCore.Qt.AA_Use96Dpi)
main = MainWindow()
main.show()
sys.exit(app.exec_())
if __name__ == '__main__':
main()
The setAttribute solution never worked for me in that way and the windll manipulation makes the gui blurry...
adding following two lines before app = QApplication(sys.argv) solved my problem:
QApplication.setHighDpiScaleFactorRoundingPolicy(Qt.HighDpiScaleFactorRoundingPolicy.PassThrough)
QtCore.QCoreApplication.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling, True)
Answers can be found here: https://github.com/pyqtgraph/pyqtgraph/issues/756
Quick Summary of this issue:
There are essentially two ways to solve this problem.
Make your app DPI-aware (by Androwei)
import ctypes
import platform
def make_dpi_aware():
if int(platform.release()) >= 8:
ctypes.windll.shcore.SetProcessDpiAwareness(True)
# add this code before "app = QtWidgets.QApplication(sys.argv)"
make_dpi_aware()
set Qt.HighDpiScaleFactorRoundingPolicy to PassThrough (by andybarry)
# add this code before "app = QtWidgets.QApplication(sys.argv)"
QtWidgets.QApplication.setAttribute(QtCore.Qt.HighDpiScaleFactorRoundingPolicy.PassThrough)
I have tried both, and they both work perfectly! Thanks to these contributors. Hope you can find this useful as well.
Related
I have a large dataset of around 500 parquet files with about 42 million samples.
In order to read those files I'm using Dask which does a great job.
In order to display them, I downsampled the Dask DataFrame in the most basic way (something like dd[::200]) and plotted it using Plotly.
So far everything works great.
Now, I had like to have an interactive figure on one side but I don't want it to open a web tab/to use jupyter/anything of this kind. I just want it to create a figure as matplotlib does.
In order to do so, I found a great solution that uses QWebEngineView:
plotly: how to make a standalone plot in a window?
My simplified code looks something like this:
import dask.dataframe as dd
import time
import plotly.graph_objects as go
def show_in_window(fig):
import sys, os
import plotly.offline
from PyQt5.QtCore import QUrl
from PyQt5.QtWebEngineWidgets import QWebEngineView
from PyQt5.QtWidgets import QApplication
plotly.offline.plot(fig, filename='temp.html', auto_open=False)
app = QApplication(sys.argv)
web = QWebEngineView()
file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "temp.html"))
web.load(QUrl.fromLocalFile(file_path))
web.show()
sys.exit(app.exec_())
def create_plot(df,x_param,y_param):
fig.add_trace(go.Scattergl(x = df[x_param] , y = df[y_param], mode ='markers'))
fig = go.Figure()
ddf = dd.read_parquet("results_parq/*.parquet")
create_data_for_plot(ddf,'t','reg',1)
fig.update_layout(showlegend=False)
show_in_window(fig)
QUESTION:
Since the dataset is large and I want to use a smarter downsample method, I would like to use a library called plotly-resampler (https://predict-idlab.github.io/plotly-resampler/getting_started.html#how-to-use) which dynamically changes the amount of samples based on the zooming level. However, it uses Dash.
I thought to do something like:
fig = FigureResampler(go.Figure())
ddf = dd.read_parquet("results_parq/*.parquet")
create_data_for_plot(ddf,'t','reg',1)
show_in_window(fig)
This creates a plot with the smart resample but it does not change its resampling when the zoom changes (it basically stuck on its initial sampling).
Is there any solution that might give me a Dash figure in a separate window instead of a tab and yet to have the functionalities of Dash?
Thank you
I believe you could store it in a local file, then use the code
import webbrowser
new = 2 # open in a new window, if possible
// open an HTML file on my own (Windows) computer
url = "file://d/testdata.html"
webbrowser.open(url,new=new)
to open it in a new window
I am using PyQtGraph and am really enjoying it, but have hit upon an issue that may force me to move to something else.
I am displaying medical images (CT/MRI etc.) as numpy 2D or 3D arrays in the ImageView which gives the nice slider view for volume data. The problem is theses images are often low res (256x256) and when viewed on large monitors or just zoomed-in they look blocky and horrible.
How can I show these images antialiased? This seems to be possible as mentioned here:
How can anti-aliasing be enabled in a pyqtgraph ImageView?
and a few other places suggesting all you need to do is:
import pyqtgraph as pg
pg.setConfigOptions(antialias=True)
and enable antialiasing in the graphics view, which I assume would be this:
myImageViewWidget = pg.ImageView(parent=None)
myImageViewWidget.ui.graphicsView.setAntialiasing(True)
But this doesn't seem to do anything different in my code. What am I doing wrong?
I'm using Windows 10 (but need it to work on MacOs - Darwin), Python 3.7, PySide 2 (5.15.12) and PyQtGraph 0.12.3
'Minimum' code to reproduce the issue (not quite but I want to keep ImageView subclassed as that's how I have it in my code):
import sys
from PySide2.QtWidgets import (
QApplication,
QHBoxLayout,
QMainWindow,
QWidget,
)
import pyqtgraph as pg
import numpy as np
pg.setConfigOptions(antialias=True)
class MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.cw = QWidget(self)
self.cw.setAutoFillBackground(True)
self.setCentralWidget(self.cw)
self.layout = QHBoxLayout()
self.cw.setLayout(self.layout)
self.ImgWidget = MyImageWidget(parent=self)
self.layout.addWidget(self.ImgWidget)
self.show()
class MyImageWidget(pg.ImageView):
def __init__(self, parent=None):
super().__init__(parent)
self.ui.histogram.hide()
self.ui.roiBtn.hide()
self.ui.menuBtn.hide()
self.ui.graphicsView.setAntialiasing(True)
# 5 frames of 50x50 random noise
img = (1000 * np.random.normal(size=(5, 50, 50))) - 500
self.setImage(img)
def main():
app = QApplication()
main = MainWindow()
main.show()
sys.exit(app.exec_())
if __name__ == '__main__':
main()
What you're referring to is not antialiasing.
Antialiasing "smoothens" the portions of an image that cannot "fit" precisely a single physical pixel.
What you are seeing is in fact the opposite, as each source pixel is actually large enough to be shown as it is: a square that possibly occupies more physical pixels.
What you probably want is a blur effect, which can be achieved through a QGraphicsBlurEffect set on the self.imageItem of the view:
class MyImageWidget(pg.ImageView):
def __init__(self, parent=None):
# ...
self.blurEffect = QGraphicsBlurEffect(blurRadius=1.1)
self.imageItem.setGraphicsEffect(self.blurEffect)
Note that since the image item is always scaled and the blur effect is proportional, you might need to adjust the blur radius to even smaller values depending on the shown resolution (but still bigger than 1.0).
Before plotting using matplotlib, you must specify your display's DPI if you have a high DPI display, since otherwise the image is too small. I have a 4K display, so I definitely need to do this. (I think that matplotlib should automatically do this for you, but that is another topic...)
As a first attempt to specify the DPI, consider the code below. It manually specifies the display's DPI and then creates and plots a test DataFrame:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sys
# method #1: manually specify my display's DPI:
dpi = 163 # this value is valid for my Dell U2718Q 4K (3840 x 2160) display
plt.rcParams["figure.dpi"] = dpi
print("plt.matplotlib.rcParams[\"figure.dpi\"] = " + str(plt.matplotlib.rcParams["figure.dpi"]))
# define a test DataFrame (here, chose to calculate sin and cos over their range of 2 pi):
n = 100
x = (2 * np.pi / n) * np.arange(n)
df = pd.DataFrame( {
"sin(x)" : np.sin(x),
"cos(x)": np.cos(x),
}
)
# plot the DataFrame:
df.plot(figsize = (12, 8), title = "sin and cos", grid = True, color = ["red", "green"])
When I put the code above into a file and run it all at once in PyCharm, everything behaves exactly as expected: the script completes without error, the plot is generated at the correct size, and the plot remains open in a window after the script ends.
So far, so good.
But the code above is brittle: run it on a computer with a different display DPI, and the image will not be sized correctly.
Doing a web search, I found this link which has code claims to automatically determine your display's DPI. My (slight) adaptation of the code is this
# method #2: call code to determine my display's DPI (only works if the backend is Qt)
if plt.get_backend() == "Qt5Agg":
from matplotlib.backends.qt_compat import QtWidgets
qApp = QtWidgets.QApplication(sys.argv)
plt.matplotlib.rcParams["figure.dpi"] = qApp.desktop().physicalDpiX()
If I modify my file to use the code above ("method #2") instead of the manual DPI setting ("method #1"), I find that the script completes without error, but the plot only comes up for a brief instant before being automatically closed!
By successively commenting out lines in the "method #2" code, starting with the last and working backwards, I have determined that the culprit is the call to QtWidgets.QApplication(sys.argv).
In particular, if I reduce the "method #2" code to just this
if plt.get_backend() == "Qt5Agg":
from matplotlib.backends.qt_compat import QtWidgets
QtWidgets.QApplication(sys.argv)
I get this plot auto close behavior.
Another defect, is that the original "method #2" code calculates the DPI of my monitor, a Dell U2718Q, to be 160, when it really is 163: in this link go to p. 3 / 4 and look at the Pixels per inch (PPI) spec.
Does anyone know of a solution to this?
Better code to determine the DPI?
A modification of the "method #2" code which will not cause plots to auto close?
Is this a bug that needs to be reported to matplotlib or Qt?
In my epic struggle to process raw mouse data on my ubuntu (14.04) OS with python, with alot help from here i am stuck again. It seems very hard for me to understand the "easyness" of pyqtgraph. all i want to do is to wrap the code i have now into a nice little gui with a start/pause/stop button, a list widget to show the numbers and a plot to let me see whats happening. I guess the main problem for me is, that i dont quite understand this whole event thing in pyqt.
anyway taking a "easy" example which has the widgets i want, i fail to implement my code(edited more minimalistic):
#!/usr/bin/python
import threading
import struct
import time
import numpy as np
from PyQt4 import QtGui # (the example applies equally well to PySide)
from PyQt4 import QtCore
import pyqtgraph as pg
##
data =[(0,0)]
sum_data = [(0,0)]
file = open("/dev/input/mouse2", "rb")
def getMouseEvent():
buf = file.read(3);
#python 2 & 3 compatibility
button = buf[0] if isinstance(buf[0], int) else ord(buf[0])
x,y = struct.unpack( "bb", buf[1:] );
print x,y
return x, y
def mouseCollect():
while True:
data.append(getMouseEvent())
sum_data.append(tuple(map(sum,zip(sum_data[-1],data[-1]))))
plot.plot(sum_data[0], clear=True)
pg.QtGui.QApplication.processEvents()
print sum_data[-1]
## Always start by initializing Qt (only once per application)
app = QtGui.QApplication([])
## Define a top-level widget to hold everything
w = QtGui.QWidget()
## Create some widgets to be placed inside
btn1 = QtGui.QPushButton('Start')
listw = QtGui.QListWidget()
plot = pg.PlotWidget()
def start_btn():
print 'test'
threading.Thread(target=mouseCollect).start()
btn1.clicked.connect(start_btn)
## Create a grid layout to manage the widgets size and position
layout = QtGui.QGridLayout()
w.setLayout(layout)
## Add widgets to the layout in their proper positions
layout.addWidget(btn1, 0, 0) # button goes in upper-left
layout.addWidget(plot, 0, 1, 4, 1)
## Display the widget as a new window
w.show()
## Start the Qt event loop
app.exec_()
##------------------------------------------------------------------
when i press the start button, the window just freezes and nothing happens. my thought was, if i press the button, it connects to the methot state there and it just doing its stuff. okay i have an infinite loop, but at least i thought i should see something. any help is apreciated, also any tips for a good reading about the matter is very welcome.
regards
Edit: inserted a thread as suggested by echocage
The best approach for repetitive updates is to use a QTimer. In this way you allow program control to go back to the Qt event loop after every update (no infinite loops allowed), and Qt periodically invokes your update function for you.
For example, see one of the many the updating plot examples included with pyqtgraph: https://github.com/pyqtgraph/pyqtgraph/blob/develop/examples/Plotting.py#L58
Threading is very difficult to do correctly and when it's done wrong you usually end up with crashing that is difficult to debug. I recommend to avoid threading until you are very confident with the event system and familiar with the pitfalls of threading.
I finally found it. I somehow never realized, that you HAVE to use numpy arrays. also i didnt use curve.setdata for plotting.
the more or less final code(not full code) now looks like this:
class mouseCollect(QtCore.QThread):
def __init__(self):
QtCore.QThread.__init__(self)
def run (self):
global e, curve, curve2, data1, data2
while e.wait():
tmp = getMouseEvent() #returns tuples in form of (x,y)
data1 = np.append(data1, tmp[0] )
data2 = np.append(data2, tmp[1] )
sum1= np.cumsum(data1)
sum2= np.cumsum(data2)
curve.setData(y=sum1)
curve2.setData(y=sum2)
guiPE # process event thingy
def stop(self):
e.clear()
well, it is a not really written efficiently, but it works :)
I want to show all attributes and tags in auto completion list of a html file if auto completion threshold is set to 1. I have tried this code to use APIs i set this code after the file is loaded in new mdi child(sub window) but it is not working:
lexer=Qsci.QsciLexerHTML()
api = Qsci.QsciAPIs(lexer)
## Add autocompletion strings
api.add("aLongString")
api.add("aLongerString")
api.add("aDifferentString")
api.add("sOmethingElse")
## Compile the api for use in the lexer
api.prepare()
self.activeMdiChild().setAutoCompletionSource(Qsci.QsciScintilla.AcsAPIs)
self.activeMdiChild().setLexer(lexer)
and my horizontal scroll bar is visible all the time i want to set it as scrollbarasneeded. please tell how to do these two tasks.
Other than failing to set the auto-completion threshold, there doesn't seem to be anything wrong with your example code. Here's a minimal working example:
from PyQt4 import QtGui, Qsci
class Window(Qsci.QsciScintilla):
def __init__(self):
Qsci.QsciScintilla.__init__(self)
lexer = Qsci.QsciLexerHTML(self)
api = Qsci.QsciAPIs(lexer)
api.add('aLongString')
api.add('aLongerString')
api.add('aDifferentString')
api.add('sOmethingElse')
api.prepare()
self.setAutoCompletionThreshold(1)
self.setAutoCompletionSource(Qsci.QsciScintilla.AcsAPIs)
self.setLexer(lexer)
if __name__ == "__main__":
import sys
app = QtGui.QApplication(sys.argv)
window = Window()
window.show()
app.exec_()
The scrollbar-as-needed feature cannot really be solved, unless you are willing to reimplement everything yourself (which would not be easy). The underlying Scintilla control doesn't directly support automatic horizontal scrollbar hiding, because it involves a potentially very expensive calculation (i.e. determining the longest line). Most people who use Scintilla/Qscintilla just learn to put up with the ever-present horizontal scrollbar.