time library not working with PySimpleGUI - numpy

here is the code:
import PySimpleGUI as pg
import time as tm
import numpy as np
num = 0
layout = [
[pg.Text('0', key='-txt')],
[pg.Button('Start')],
]
timer_nums = np.arange(0.1, 1, .1)
win = pg.Window('Stopwatch', layout)
while True:
e, v = win.read()
if e == pg.WIN_CLOSED:
break
if e == 'Start':
tm.sleep(.14)
for i in timer_nums:
win['-txt'].update(round(i, 3))
print(round(i, 3))
tm.sleep(.1)
win.close()
So I'm trying to use the tm.sleep() to delay the count on a stopwatch. But each time i run the program the gui freezes, so i have the print function in so i can see the console telling me it's working right but the gui won't update txt until the for loop finishes. Which kinda defeats the purpose of the program lol. Please help

Related

Is there a way to have multiple rectangles displayed at the same time using visual.Rect in PsychoPy?

I'm trying to create stimuli that consist of 100 small lines in the centre of the screen, with orientations sampled from a Gaussian distribution (please see the image link below):
Orientation stimuli
I've managed to achieve something that almost fits the bill, but this code only works in isolation:
from psychopy import visual, core, event
import numpy as np
from numpy.random import random
import random
Lines = visual.Rect(
win=win, name='Lines',
width=(0.015, 0.0025)[0], height=(0.015, 0.0025)[1],
lineWidth=1, lineColor=[1,1,1], lineColorSpace='rgb',
fillColor=[1,1,1], fillColorSpace='rgb',
opacity=1, depth=-2.0, interpolate=True)
lines_hpos = np.random.uniform(-0.49,0.49,100)
mu = 315
sigma = 15
for i in range(100):
Lines.pos = [lines_hpos[i],np.random.uniform(-0.49,0.49)]
Lines.ori = random.gauss(mu, sigma)
I've tried to manipulate this code so that I can integrate it into the full experiment I'm designing in PsychoPy's experiment builder. I run the below code in the experiment builder's coding window calling 'gdist' and 'loc' as values for the 'Orientation' and 'Position' of the rectangles, respectively:
import random
gdist =[]
loc = []
lines_hpos = np.random.uniform(-0.49,0.49,100)
mu = 90
sigma = 20
for i in range(100):
rloc = [lines_hpos[i],np.random.uniform(-0.49,0.49)]
loc.append(rloc)
gauss = random.gauss(mu, sigma)
gdist.append(gauss)
When I attempt to run the experiment, I get an error return and the experiment fails to start:
File "C:\Users\r02mj20\AppData\Local\PsychoPy3\lib\site-packages\psychopy\visual\image.py", line 238, in __del__
File "C:\Users\r02mj20\AppData\Local\PsychoPy3\lib\site-packages\pyglet\gl\lib.py", line 97, in errcheck
ImportError: sys.meta_path is None, Python is likely shutting down
I'm assuming this has something to do with pyglet not liking the idea of there being 100 rectangles all at once (side note: the script works fine if range(1)). If anyone has any suggestions for how I might fix or work around this problem, I'd be eternally grateful.
i don't see any problem with this idea, except you better use visual.Line instead of Rect, and your units of measure are not described; the key to preserving video memory is BufferImageStim, btw
from psychopy import visual, core, event, monitors
from psychopy.iohub.client import launchHubServer
import random
import numpy as np
MU = 315; SIGMA = 15
num_lines = 100
io = launchHubServer(iohub_config_name='iohub_config.yaml')
display = io.devices.display
mon = monitors.Monitor(name = display.getPsychopyMonitorName())
win = visual.Window([640, 480], units='pix', viewScale = 1.0,
monitor = mon, winType='pyglet',
fullScr = False, waitBlanking = True, useFBO = True, useLights = False,
allowStencil=False, allowGui = True,
screen = display.getIndex(), colorSpace = 'rgb255', color = [128,128,128],
name = 'my_win01')
rects = []
lines_hpos = np.random.uniform(-0.49, 0.49, num_lines)
for i in range(num_lines):
line_rect = visual.Rect(win=win, size=(0.001, 1.0), units='norm',
pos=(0,0), lineWidth=1, lineColor=[1,1,1], fillColor=[1,1,1], opacity=1, depth=-2.0,
name='lines_rect', interpolate=True, autoLog=False, autoDraw=False)
line_rect.pos = [lines_hpos[i], np.random.uniform(-0.49,0.49)]
line_rect.ori = random.gauss(MU, SIGMA)
rects.append(line_rect)
rect_buffer = visual.BufferImageStim(win, buffer='back', stim=rects, sqPower2=False, interpolate=False, name='rect-buffer', autoLog=True)
rect_buffer.draw()
win.flip()
event.waitKeys()

How to use hover events in mpl_connect in matplotlib

I'm working on line plotting a metric for a course module as well as each of its questions within a Jupyter Notebook using %matplotlib notebook. That part is no problem. A module has typically 20-35 questions, so it results in a lot of lines on a chart. Therefore, I am plotting the metric for each question in a low alpha and I want to change the alpha and display the question name when I hover over the line, then reverse those when no longer hovering over the line.
The thing is, I've tried every test version of interactivity from the matplotlib documentation on event handling, as well as those in this question. It seems like the mpl_connect event is never firing, whether I use click or hover.
Here's a test version with a reduced dataset using the solution to the question linked above. Am I missing something necessary to get events to fire?
def update_annot(ind):
x,y = line.get_data()
annot.xy = (x[ind["ind"][0]], y[ind["ind"][0]])
text = "{}, {}".format(" ".join(list(map(str,ind["ind"]))),
" ".join([names[n] for n in ind["ind"]]))
annot.set_text(text)
annot.get_bbox_patch().set_alpha(0.4)
def hover(event):
vis = annot.get_visible()
if event.inaxes == ax:
cont, ind = line.contains(event)
if cont:
update_annot(ind)
annot.set_visible(True)
fig.canvas.draw_idle()
else:
if vis:
annot.set_visible(False)
fig.canvas.draw_idle()
module = 'bd2bc472-ee0d-466f-8557-788cc6de3018'
module_metrics[module] = {
'q_count': 31,
'sequence_pks': [0.5274546300604932,0.5262044653349001,0.5360993905297703,0.5292329279700655,0.5268691588785047,0.5319099014547161,0.5305164319248826,0.5268235294117647,0.573648805381582,0.5647933116581514,0.5669839795681448,0.5646591970121382,0.5663157894736842,0.5646976090014064,0.5659005628517824,0.5693634879925391,0.5728268468888371,0.5668834184858337,0.5687237026647967,0.5795640965549567,0.5877684407096172,0.585690904839841,0.5766899766899767,0.5971341320178529,0.6059972105997211,0.6055516678329834,0.6209865053513262,0.6203121360354065,0.6153666510976179,0.6236909471724459,0.6387654898293196],
'q_pks': {
'0da04f02-4aad-4ac8-91a5-214862b5c0d0': [0.6686046511627907,0.6282051282051282,0.76,0.6746987951807228,0.7092198581560284,0.71875,0.6585365853658537,0.7070063694267515,0.7171052631578947,0.7346938775510204,0.7737226277372263,0.7380952380952381,0.6774193548387096,0.7142857142857143,0.7,0.6962962962962963,0.723404255319149,0.6737588652482269,0.7232704402515723,0.7142857142857143,0.7164179104477612,0.7317073170731707,0.6333333333333333,0.75,0.7217391304347827,0.7017543859649122,0.7333333333333333,0.7641509433962265,0.6869565217391305,0.75,0.794392523364486],
'10bd29aa-3a26-49e6-bc2c-50fd503d7ab5': [0.64375,0.6014492753623188,0.5968992248062015,0.5059523809523809,0.5637583892617449,0.5389221556886228,0.5576923076923077,0.51875,0.4931506849315068,0.5579710144927537,0.577922077922078,0.5467625899280576,0.5362318840579711,0.6095890410958904,0.5793103448275863,0.5159235668789809,0.6196319018404908,0.6143790849673203,0.5035971223021583,0.5897435897435898,0.5857142857142857,0.5851851851851851,0.6164383561643836,0.6054421768707483,0.5714285714285714,0.627906976744186,0.5826771653543307,0.6504065040650406,0.5864661654135338,0.6333333333333333,0.6851851851851852]
}}
suptitle_size = 24
title_size = 18
tick_size = 12
axis_label_size = 15
legend_size = 14
fig, ax = plt.subplots(figsize=(15,8))
fig.suptitle('PK by Sequence Order', fontsize=suptitle_size)
module_name = 'Test'
q_count = module_metrics[module]['q_count']
y_ticks = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
x_ticks = np.array([x for x in range(0,q_count)])
x_labels = x_ticks + 1
# Plot it
ax.set_title(module_name, fontsize=title_size)
ax.set_xticks(x_ticks)
ax.set_yticks(y_ticks)
ax.set_xticklabels(x_labels, fontsize=tick_size)
ax.set_yticklabels(y_ticks, fontsize=tick_size)
ax.set_xlabel('Sequence', fontsize=axis_label_size)
ax.set_xlim(-0.5,q_count-0.5)
ax.set_ylim(0,1)
ax.grid(which='major',axis='y')
# Output module PK by sequence
ax.plot(module_metrics[module]['sequence_pks'])
# Output PK by sequence for each question
for qid in module_metrics[module]['q_pks']:
ax.plot(module_metrics[module]['q_pks'][qid], alpha=0.15, label=qid)
annot = ax.annotate("", xy=(0,0), xytext=(-20,20),textcoords="offset points", bbox=dict(boxstyle="round", fc="w"), arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)
mpl_id = fig.canvas.mpl_connect('motion_notify_event', hover)
Since there are dozens of modules, I created an ipywidgets dropdown to select the module, which then runs a function to output the chart. Nonetheless, whether running it hardcoded as here or from within the function, mpl_connect never seems to fire.
Here's what this one looks like when run

Python multiprocessing how to update a complex object in a manager list without using .join() method

I started programming in Python about 2 months ago and I've been struggling with this problem in the last 2 weeks.
I know there are many similar threads to this one but I can't really find a solution which suits my case.
I need to have the main process which is the one which interacts with Telegram and another process, buffer, which understands the complex object received from the main and updates it.
I'd like to do this in a simpler and smoother way.
At the moment objects are not being updated due to the use of multi-processing without the join() method.
I tried then to use multi-threading instead but it gives me compatibility problems with Pyrogram a framework which i am using to interact with Telegram.
I wrote again the "complexity" of my project in order to reproduce the same error I am getting and in order to get and give the best help possible from and for everyone.
a.py
class A():
def __init__(self, length = -1, height = -1):
self.length = length
self.height = height
b.py
from a import A
class B(A):
def __init__(self, length = -1, height = -1, width = -1):
super().__init__(length = -1, height = -1)
self.length = length
self.height = height
self.width = width
def setHeight(self, value):
self.height = value
c.py
class C():
def __init__(self, a, x = 0, y = 0):
self.a = a
self.x = x
self.y = y
def func1(self):
if self.x < 7:
self.x = 7
d.py
from c import C
class D(C):
def __init__(self, a, x = 0, y = 0, z = 0):
super().__init__(a, x = 0, y = 0)
self.a = a
self.x = x
self.y = y
self.z = z
def func2(self):
self.func1()
main.py
from b import B
from d import D
from multiprocessing import Process, Manager
from buffer import buffer
if __name__ == "__main__":
manager = Manager()
lizt = manager.list()
buffer = Process(target = buffer, args = (lizt, )) #passing the list as a parameter
buffer.start()
#can't invoke buffer.join() here because I need the below code to keep running while the buffer process takes a few minutes to end an instance passed in the list
#hence I can't wait the join() function to update the objects inside the buffer but i need objects updated in order to pop them out from the list
import datetime as dt
t = dt.datetime.now()
#library of kind of multithreading (pool of 4 processes), uses asyncio lib
#this while was put to reproduce the same error I am getting
while True:
if t + dt.timedelta(seconds = 10) < dt.datetime.now():
lizt.append(D(B(5, 5, 5)))
t = dt.datetime.now()
"""
#This is the code which looks like the one in my project
#main.py
from pyrogram import Client #library of kind of multithreading (pool of 4 processes), uses asyncio lib
from b import B
from d import D
from multiprocessing import Process, Manager
from buffer import buffer
if __name__ == "__main__":
api_id = 1234567
api_hash = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
app = Client("my_account", api_id, api_hash)
manager = Manager()
lizt = manager.list()
buffer = Process(target = buffer, args = (lizt, )) #passing the list as a parameter
buffer.start()
#can't invoke buffer.join() here because I need the below code to run at the same time as the buffer process
#hence I can't wait the join() function to update the objects inside the buffer
#app.on_message()
def my_handler(client, message):
lizt.append(complex_object_conatining_message)
"""
buffer.py
def buffer(buffer):
print("buffer was defined")
while True:
if len(buffer) > 0:
print(buffer[0].x) #prints 0
buffer[0].func2() #this changes the class attribute locally in the class instance but not in here
print(buffer[0].x) #prints 0, but I'd like it to be 7
print(buffer[0].a.height) #prints 5
buffer[0].a.setHeight(10) #and this has the same behaviour
print(buffer[0].a.height) #prints 5 but I'd like it to be 10
buffer.pop(0)
This is the whole code about the problem I am having.
Literally every suggestion is welcome, hopefully constructive, thank you in advance!
At last I had to change the way to solve this problem, which was using asyncio like the framework was doing as well.
This solution offers everything I was looking for:
-complex objects update
-avoiding the problems of multiprocessing (in particular with join())
It is also:
-lightweight: before I had 2 python processes 1) about 40K 2) about 75K
This actual process is about 30K (and it's also faster and cleaner)
Here's the solution, I hope it will be useful for someone else like it was for me:
The part of the classes is skipped because this solution updates complex objects absolutely fine
main.py
from pyrogram import Client
import asyncio
import time
def cancel_tasks():
#get all task in current loop
tasks = asyncio.Task.all_tasks()
for t in tasks:
t.cancel()
try:
buffer = []
firstWorker(buffer) #this one is the old buffer.py file and function
#the missing loop and loop method are explained in the next piece of code
except KeyboardInterrupt:
print("")
finally:
print("Closing Loop")
cancel_tasks()
firstWorker.py
import asyncio
def firstWorker(buffer):
print("First Worker Executed")
api_id = 1234567
api_hash = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
app = Client("my_account", api_id, api_hash)
#app.on_message()
async def my_handler(client, message):
print("Message Arrived")
buffer.append(complex_object_conatining_message)
await asyncio.sleep(1)
app.run(secondWorker(buffer)) #here is the trick: I changed the
#method run() of the Client class
#inside the Pyrogram framework
#since it was a loop itself.
#In this way I added another task
#to the existing loop in orther to
#let run both of them together.
my secondWorker.py
import asyncio
async def secondWorker(buffer):
while True:
if len(buffer) > 0:
print(buffer.pop(0))
await asyncio.sleep(1)
The resources to understand the asyncio used in this code can be found here:
Asyncio simple tutorial
Python Asyncio Official Documentation
This tutorial about how to fix classical Asyncio errors

how to make cross hair mouse tracker on a PlotWidget() promoted in designer-qt5

I am trying to make a cross hair on my pyqtgraph interactive plots, which are embedded in a PyQt5 GUI thanks to the designer-qt5. I found a working
code in the pyqtgraph "examples". A simplified WORKING example is posted below. Now I want the same, but the problem seems to be that I promoted a
QGraphicsView() to a pg.PlotWidget in the designer, instead of pg.GraphicsWindow()? The Code does not work for me because my p1 is "pyqtgraph.widgets.PlotWidget.PlotWidget object" while in the example p1 is
"pyqtgraph.graphicsItems.PlotItem.PlotItem.PlotItem object".
So what should I do to make this example work for me?
import numpy as np
import pyqtgraph as pg
from pyqtgraph.Qt import QtGui, QtCore
from pyqtgraph.Point import Point
pg.setConfigOption('background', '#ffffff')
pg.setConfigOption('foreground', 'k')
pg.setConfigOptions(antialias=True)
app = QtGui.QApplication([])
win = pg.GraphicsWindow()
win.setWindowTitle('pyqtgraph example: crosshair')
label = pg.LabelItem(justify='right')
win.addItem(label)
p1 = win.addPlot(row=1, col=0)
p1.setAutoVisible(y=True)
#create numpy arrays
#make the numbers large to show that the xrange shows data from 10000 to all the way 0
data1 = 10000 + 15000 * pg.gaussianFilter(np.random.random(size=10000), 10) + 3000 * np.random.random(size=10000)
p1.plot(data1, pen="r")
#cross hair
vLine = pg.InfiniteLine(angle=90, movable=False)
hLine = pg.InfiniteLine(angle=0, movable=False)
p1.addItem(vLine, ignoreBounds=True)
p1.addItem(hLine, ignoreBounds=True)
vb = p1.vb
print(p1)
print(vb)
def mouseMoved(evt):
pos = evt[0] ## using signal proxy turns original arguments into a tuple
if p1.sceneBoundingRect().contains(pos):
mousePoint = vb.mapSceneToView(pos)
index = int(mousePoint.x())
if index > 0 and index < len(data1):
label.setText("<span style='font-size: 12pt'>x=%0.1f, <span style='color: green'>y2=%0.1f</span>" % (mousePoint.x(), data1[index]))
vLine.setPos(mousePoint.x())
hLine.setPos(mousePoint.y())
proxy = pg.SignalProxy(p1.scene().sigMouseMoved, rateLimit=60, slot=mouseMoved)
#p1.scene().sigMouseMoved.connect(mouseMoved)
## 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_()
I am very sorry for the noise!!! I fix it myself!
The important part was:
plot_wg.proxy = proxy
Very simple...
Below is the function, which should work for any PlotWidget:
def cross_hair(self, plot_wg, log=False ):
global fit
################### TETS cross hair ############3
vLine = pg.InfiniteLine(angle=90, movable=False)#, pos=0)
hLine = pg.InfiniteLine(angle=0, movable=False)#, pos=2450000)
plot_wg.addItem(vLine, ignoreBounds=True)
plot_wg.addItem(hLine, ignoreBounds=True)
vb = plot_wg.getViewBox()
label = pg.TextItem()
plot_wg.addItem(label)
def mouseMoved(evt):
pos = evt[0] ## using signal proxy turns original arguments into a tuple
if plot_wg.sceneBoundingRect().contains(pos):
mousePoint = vb.mapSceneToView(pos)
if log == True:
label.setText("x=%0.3f, y1=%0.3f"%(10**mousePoint.x(), mousePoint.y()))
else:
label.setText("x=%0.3f, y1=%0.3f"%(mousePoint.x(), mousePoint.y()))
vLine.setPos(mousePoint.x())
hLine.setPos(mousePoint.y())
#print(mousePoint.x(),mousePoint.y())
plot_wg.getViewBox().setAutoVisible(y=True)
proxy = pg.SignalProxy(plot_wg.scene().sigMouseMoved, rateLimit=60, slot=mouseMoved)
plot_wg.proxy = proxy
proxy = pg.SignalProxy(plot_wg.scene().sigMouseMoved, rateLimit=60, slot=mouseMoved)
plot_wg.proxy = proxy
################### TETS cross hair ############3

Multiprocessing shared numpy array

I need to share numpy array between Processes, to store in it some results. Im not quite sure if what I have done so far is correct. This is my simplified code.
from multiprocessing import Process, Lock, Array
import numpy as np
def worker(shared,lock):
numpy_arr = np.frombuffer(shared.get_obj())
# do some work ...
with lock:
for i in range(10):
numpy_arr[0] += 1
numpy_arr += 1
return
if __name__ == '__main__':
jobs = []
lock = Lock()
shared_array = Array('d', 1000000)
for process in range(4):
p = Process(target=worker, args=(shared_array,lock))
jobs.append(p)
p.start()
for process in jobs:
process.join()
m = np.frombuffer(shared_array.get_obj())
np.save('data', m)
print (m[:5])
From this code i obtain expected results, but again, Im not sure if this is the correct way. And finally, what is the diffrence between multiprocessing.Array and multiprocessing.sharedctypes.Array ?