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Cannot Display two custom Progress Bars at once

I'm currenty working on a progress bar for another project. I pretty much have everything done and working as intended.
However, i am having an issue, where i can't have two (or more) bars displaying simultaneously. The second one always seems to overwrite the first.
Here's the code (excuse my crappy coding skills):
Progress Bar Class:
import sys
class ProgressBar():
full_size = 100
standard = 50
small = 25
def __init__(self, pretext, length=50, total=100):
self.pretext = pretext
self.length = length
self.total = total
def update_bar(self, fill_amount):
real_fill_amount = int(self.length * fill_amount / self.total)
sys.stdout.write("\r%s |%s%s| %s" % (self.pretext,
"█" * real_fill_amount,
"░" * (self.length - real_fill_amount),
str(fill_amount) + "%"))
sys.stdout.flush()
Example code (CPU and Memory Usage Meter):
from pout_progressbar import ProgressBar
import psutil
import time
bar1 = ProgressBar("CPU Usage", length=ProgressBar.standard, total=100)
bar2 = ProgressBar("Memory Usage", length=ProgressBar.standard, total=100)
while True:
bar1.update_bar(int(psutil.cpu_percent(interval=1)))
bar2.update_bar(int(psutil.virtual_memory().percent))
time.sleep(0.5)
Note: pout_progressbar is the filename of the ProgressBar module.
In the example script, two bars are supposed to be showing, but only the second one is showing.
Is there any way i can fix this ?
Thanks in advance.

That is because your second progress bar output overwrites that of the first. Here is a way to fix that:
import sys
import psutil
import time
class ProgressBar:
full_size = 100
standard = 50
small = 25
def __init__(self, pretext, length=50, total=100):
self.pretext = pretext
self.length = length
self.total = total
def update_bar(self, fill_amount):
real_fill_amount = self.length * fill_amount // self.total
progress = f"{self.pretext} |{'█'*real_fill_amount}" \
f"{'░'*(self.length - real_fill_amount)}| {fill_amount}%"
return progress
bar1 = ProgressBar("CPU Usage")
bar2 = ProgressBar("Memory Usage")
while True:
progress1 = bar1.update_bar(int(psutil.cpu_percent(interval=1)))
progress2 = bar2.update_bar(int(psutil.virtual_memory().percent))
sys.stdout.write(f'\r{progress1}\t{progress2}')
sys.stdout.flush()
time.sleep(0.5)
However, there is another problem. It is not trivial to overwrite more than just the previous line. So for now, both progess bars are displayed in the same line. If that is not ok for you, you need to look into one of the options to overwrite multiple lines, however how to do that is system dependent.

As output of next bar depends on previous one, separating these is not able. So if I write:
class ProgressBars():
full_size = 100
standard = 50
small = 25
def __init__(self, pretexts, length=50, total=100):
self.pretexts = pretexts
self.length = length
self.total = total
def update_bar(self, fill_amounts):
real_fill_amounts = [int(self.length * x / self.total) for x in fill_amounts]
line = "%s |%s%s| %s" % (self.pretext,
"█" * real_fill_amount,
"░" * (self.length - real_fill_amount))
str(fill_amount) + "%"))
sys.stdout.write("\b" * (len(line) + len(fill_amounts))
sys.stdout.write(line)
sys.stdout.flush()
If you wan to control console in advance, There is curses.

How can i use the return result from function?

I'm learing python and I'm not sure how to solve my problem. I created a class and definition that generates an array with numbers, then i want to create another def that will multiply each number array. I tried something like this, but i got the error.
class Hydrology():
def wieloktornosc_tm():
tab = list(i for i in range(600, 43800, 600))
return tab
def wielokrotnosc_nat_deszczu():
wielokrotnosc_q = []
for i in self.wieloktornosc_tm():
q = (15.347 * (1013 / i ** 0.667))
return wielokrotnosc_q.append(q)
print(Hydrologia.wielokrotnosc_tm())
print(Hydrologia.wieloktornosc_nat_deszczu())

Maybe you meant to do this?
def wielokrotnosc_nat_deszczu():
wielokrotnosc_q = []
for i in self.wieloktornosc_tm():
q = (15.347 * (1013 / i ** 0.667))
wielokrotnosc_q.append(q)
return wielokrotnosc_q
With your code it will always return None
As suggested in the comments you can achieve the same effect using a standalone function like so:
def wielokrotnosc_nat_deszczu():
wielokrotnosc_q = []
for i in range(600, 43800, 600):
q = (15.347 * (1013 / i ** 0.667))
wielokrotnosc_q.append(q)
return wielokrotnosc_q
Using another function to create the tab would make sense if you are passing parameters to the function that will produce different results as a consequence of the parameters.

There is a lot of errors (wrong names, missing self in method names, not appending in a loop) in your snippet, this code is working:
class Hydrology():
#staticmethod
def wielokrotnosc_tm():
tab = list(i for i in range(600, 43800, 600))
return tab
#classmethod
def wielokrotnosc_nat_deszczu(cls):
wielokrotnosc_q = []
for i in cls.wielokrotnosc_tm():
q = (15.347 * (1013 / i ** 0.667))
wielokrotnosc_q.append(q)
return wielokrotnosc_q
print(Hydrology.wielokrotnosc_tm())
print(Hydrology.wielokrotnosc_nat_deszczu())
If you want to discuss changes wirte me a pm

Try this variation instead:
class Hydrology():
# You need to have the self keyword in every
# method definition and in case you need
# to add arguments then (self, arg1, arg2, ...etc)
def wieloktornosc_tm(self):
tab = [i for i in range(600, 43800, 600)]
return tab
def wielokrotnosc_nat_deszczu(self):
wielokrotnosc_q = []
for i in self.wieloktornosc_tm():
q = (15.347 * (1013 / i ** 0.667))
return wielokrotnosc_q.append(q)
# You need to create the class object first.
Hydrologia = Hydrology()
# And then access its methods
print Hydrologia.wieloktornosc_tm()
print Hydrologia.wielokrotnosc_nat_deszczu()

Using click.progressbar with multiprocessing in Python

I have a huge list that I need to process, which takes some time, so I divide it into 4 pieces and multiprocess each piece with some function. It still takes a bit of time to run with 4 cores, so I figured I would add some progress bar to the function, so that it could tell me where each processor is at in processing the list.
My dream was to have something like this:
erasing close atoms, cpu0 [######..............................] 13%
erasing close atoms, cpu1 [#######.............................] 15%
erasing close atoms, cpu2 [######..............................] 13%
erasing close atoms, cpu3 [######..............................] 14%
with each bar moving as the loop in the function progresses. But instead, I get a continuous flow:
etc, filling my terminal window.
Here is the main python script that calls the function:
from eraseCloseAtoms import *
from readPDB import *
import multiprocessing as mp
from vectorCalc import *
prot, cell = readPDB('file')
atoms = vectorCalc(cell)
output = mp.Queue()
# setup mp to erase grid atoms that are too close to the protein (dmin = 2.5A)
cpuNum = 4
tasks = len(atoms)
rangeSet = [tasks / cpuNum for i in range(cpuNum)]
for i in range(tasks % cpuNum):
rangeSet[i] += 1
rangeSet = np.array(rangeSet)
processes = []
for c in range(cpuNum):
na, nb = (int(np.sum(rangeSet[:c] + 1)), int(np.sum(rangeSet[:c + 1])))
processes.append(mp.Process(target=eraseCloseAtoms, args=(prot, atoms[na:nb], cell, 2.7, 2.5, output)))
for p in processes:
p.start()
results = [output.get() for p in processes]
for p in processes:
p.join()
atomsNew = results[0] + results[1] + results[2] + results[3]
Below is the function eraseCloseAtoms():
import numpy as np
import click
def eraseCloseAtoms(protein, atoms, cell, spacing=2, dmin=1.4, output=None):
print 'just need to erase close atoms'
if dmin > spacing:
print 'the spacing needs to be larger than dmin'
return
grid = [int(cell[0] / spacing), int(cell[1] / spacing), int(cell[2] / spacing)]
selected = list(atoms)
with click.progressbar(length=len(atoms), label='erasing close atoms') as bar:
for i, atom in enumerate(atoms):
bar.update(i)
erased = False
coord = np.array(atom[6])
for ix in [-1, 0, 1]:
if erased:
break
for iy in [-1, 0, 1]:
if erased:
break
for iz in [-1, 0, 1]:
if erased:
break
for j in protein:
protCoord = np.array(protein[int(j)][6])
trueDist = getMinDist(protCoord, coord, cell, vectors)
if trueDist <= dmin:
selected.remove(atom)
erased = True
break
if output is None:
return selected
else:
output.put(selected)

accepted answer says it's impossible with click and it'd require 'non trivial amount of code to make it work'.
While it's true, there is another module with this functionality out of the box: tqdm
https://github.com/tqdm/tqdm which does exatly what you need.
You can do nested progress bars in docs https://github.com/tqdm/tqdm#nested-progress-bars etc.

I see two issues in your code.
The first one explains why your progress bars are often showing 100% rather than their real progress. You're calling bar.update(i) which advances the bar's progress by i steps, when I think you want to be updating by one step. A better approach would be to pass the iterable to the progressbar function and let it do the updating automatically:
with click.progressbar(atoms, label='erasing close atoms') as bar:
for atom in bar:
erased = False
coord = np.array(atom[6])
# ...
However, this still won't work with multiple processes iterating at once, each with its own progress bar due to the second issue with your code. The click.progressbar documentation states the following limitation:
No printing must happen or the progress bar will be unintentionally destroyed.
This means that whenever one of your progress bars updates itself, it will break all of the other active progress bars.
I don't think there is an easy fix for this. It's very hard to interactively update a multiple-line console output (you basically need to be using curses or a similar "console GUI" library with support from your OS). The click module does not have that capability, it can only update the current line. Your best hope would probably be to extend the click.progressbar design to output multiple bars in columns, like:
CPU1: [###### ] 52% CPU2: [### ] 30% CPU3: [######## ] 84%
This would require a non-trivial amount of code to make it work (especially when the updates are coming from multiple processes), but it's not completely impractical.

For anybody coming to this later. I created this which seems to work okay. It overrides click.ProgressBar fairly minimally, although I had to override an entire method for only a few lines of code at the bottom of the method. This is using \x1b[1A\x1b[2K to clear the progress bars before rewriting them so may be environment dependent.
#!/usr/bin/env python
import time
from typing import Dict
import click
from click._termui_impl import ProgressBar as ClickProgressBar, BEFORE_BAR
from click._compat import term_len
class ProgressBar(ClickProgressBar):
def render_progress(self, in_collection=False):
# This is basically a copy of the default render_progress with the addition of in_collection
# param which is only used at the very bottom to determine how to echo the bar
from click.termui import get_terminal_size
if self.is_hidden:
return
buf = []
# Update width in case the terminal has been resized
if self.autowidth:
old_width = self.width
self.width = 0
clutter_length = term_len(self.format_progress_line())
new_width = max(0, get_terminal_size()[0] - clutter_length)
if new_width < old_width:
buf.append(BEFORE_BAR)
buf.append(" " * self.max_width)
self.max_width = new_width
self.width = new_width
clear_width = self.width
if self.max_width is not None:
clear_width = self.max_width
buf.append(BEFORE_BAR)
line = self.format_progress_line()
line_len = term_len(line)
if self.max_width is None or self.max_width < line_len:
self.max_width = line_len
buf.append(line)
buf.append(" " * (clear_width - line_len))
line = "".join(buf)
# Render the line only if it changed.
if line != self._last_line and not self.is_fast():
self._last_line = line
click.echo(line, file=self.file, color=self.color, nl=in_collection)
self.file.flush()
elif in_collection:
click.echo(self._last_line, file=self.file, color=self.color, nl=in_collection)
self.file.flush()
class ProgressBarCollection(object):
def __init__(self, bars: Dict[str, ProgressBar], bar_template=None, width=None):
self.bars = bars
if bar_template or width:
for bar in self.bars.values():
if bar_template:
bar.bar_template = bar_template
if width:
bar.width = width
def __enter__(self):
self.render_progress()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.render_finish()
def render_progress(self, clear=False):
if clear:
self._clear_bars()
for bar in self.bars.values():
bar.render_progress(in_collection=True)
def render_finish(self):
for bar in self.bars.values():
bar.render_finish()
def update(self, bar_name: str, n_steps: int):
self.bars[bar_name].make_step(n_steps)
self.render_progress(clear=True)
def _clear_bars(self):
for _ in range(0, len(self.bars)):
click.echo('\x1b[1A\x1b[2K', nl=False)
def progressbar_collection(bars: Dict[str, ProgressBar]):
return ProgressBarCollection(bars, bar_template="%(label)s [%(bar)s] %(info)s", width=36)
#click.command()
def cli():
with click.progressbar(length=10, label='bar 0') as bar:
for i in range(0, 10):
time.sleep(1)
bar.update(1)
click.echo('------')
with ProgressBar(iterable=None, length=10, label='bar 1', bar_template="%(label)s [%(bar)s] %(info)s") as bar:
for i in range(0, 10):
time.sleep(1)
bar.update(1)
click.echo('------')
bar2 = ProgressBar(iterable=None, length=10, label='bar 2')
bar3 = ProgressBar(iterable=None, length=10, label='bar 3')
with progressbar_collection({'bar2': bar2, 'bar3': bar3}) as bar_collection:
for i in range(0, 10):
time.sleep(1)
bar_collection.update('bar2', 1)
for i in range(0, 10):
time.sleep(1)
bar_collection.update('bar3', 1)
if __name__ == "__main__":
cli()

It may not be the same as your dream, but you can use imap_unordered with click.progressbar to integrate with multiprocessing.
import multiprocessing as mp
import click
import time
def proc(arg):
time.sleep(arg)
return True
def main():
p = mp.Pool(4)
args = range(4)
results = p.imap_unordered(proc, args)
with click.progressbar(results, length=len(args)) as bar:
for result in bar:
pass
if __name__ == '__main__:
main()

Something like this will work if you are okay with having one progress bar:
import click
import threading
import numpy as np
reallybiglist = []
numthreads = 4
def myfunc(listportion, bar):
for item in listportion:
# do a thing
bar.update(1)
with click.progressbar(length=len(reallybiglist), show_pos=True) as bar:
threads = []
for listportion in np.split(reallybiglist, numthreads):
thread = threading.Thread(target=myfunc, args=(listportion, bar))
thread.start()
threads.append(thread)
for thread in threads:
thread.join()

AttributeError: 'int' object has no attribute Python

I dont know why i get this error and it's really annoying... anyone see the problem?
I get this error:
line 66, in <module>
ting.movefigure(ting, "up", 20)
AttributeError: 'int' object has no attribute 'movefigure'
Here is my code:
from tkinter import * import time
def movefigure(self, direction, ammount):
x = 0
y = 0
ammount2 = 0
if direction == "up":
print("Direction = " + ammount)
y = ammount
elif direction == "down":
print("Direction = " + ammount)
ammount2 = ammount - (ammount * 2)
y = ammount2
elif direction == "right" + ammount:
print("Direction = " + ammount)
x = ammount
elif direction == "left":
print("Direction = " + ammount)
ammount2 = ammount - (ammount * 2)
y = ammount2
canvas.move(self, x, y)
root = Tk()
root.title('Canvas')
tingx = 100
tingy = 100
tingxMove = 1
tingyMove = 1
canvas = Canvas(root, width=400, height=400)
ting = canvas.create_rectangle(205, 10, tingx, tingy, tags="Ting", outline='black', fill='gray50')
canvas.pack()
ting.movefigure(ting, "up", 20)
root.mainloop()

You're mixing up functions and methods.
A method is a function defined in a class; it takes a self argument, and you call it on an instance of that class. Like this:
class Spam(object):
def __init__(self, eggs):
self.eggs = eggs
def method(self, beans):
return self.eggs + beans
spam = Spam(20)
print(spam.method(10))
This will print out 30.
But your movefigure is not a method of any class, it's just a regular function. That means it doesn't take a self parameter, and you don't call it with dot syntax. (Of course there's nothing stopping you from calling any parameter self if you want, just like there's nothing stopping you from writing a function called print_with_color that erases a file named /kernel, but it's not a good idea…)
So, you wanted to do this:
def movefigure(rect, direction, ammount):
# all of your existing code, but using rect instead of self
movefigure(ting, "up", 20)

How to interact with mayavi window while animating particles? (during Particle Swarm Optimization)

I am trying to do an animation of a Particle Swarm Optimization using Python and Mayavi2.
The animation is working fine, my problem is that it is not possible to interact with the plot while it is animating the movement. Specifically i would like to turn the graph and zoom. Maybe someone has experience doing animations?
The way i do it is first to calculate the positions of the particles and then to store them. After the calculation is finished i plot the positions of the particle at the first instace of time with point3d() and then i iterate through time updating the data using the set() method.
Is there a way to make it possible to turn the graph? I have heard about something with threads, disabeling the the rendering, but i could not figure out how to do it in my code. Besides lots of other stuff, I have read:
http://code.enthought.com/projects/mayavi//docs/development/html/mayavi/mlab_animating.html
http://code.enthought.com/projects/mayavi//docs/development/html/mayavi/tips.html#acceleration-mayavi-scripts
but it can't see how to use it.
Any suggestions?
Here is my code:
#!/usr/bin/env python
'''
#author rt
'''
import pylab as plt
from numpy import *
from mayavi import mlab
from threading import Thread # making plotting faster?
import ackley as ac
class Swarm(Thread, object):
'''
constructor for the swarm
initializes all instance variables
'''
def __init__(self,objective_function):
Thread.__init__(self)
# optimization options
self.omega = 0.9 # inertial constant
self.c1 = 0.06 # cognitive/private constant
self.c2 = 0.06 # social constant
self.objective = objective_function # function object
self.max_iteration = 100 # maximal number of iterations
# Swarm stuff
self.number = 0
self.best = [] # gbest; the global best position
self.particles = [] # empty list for particles
# temporary
self.min = self.objective.min
self.max = self.objective.max
self.best_evolution = []
# self.dimensions = 2 # dimensions NB!
'''
add particles to the swarm
find the best position of particle in swarm to set global best
'''
def add_particles(self, n):
for i in range(n):
particle = Particle(self)
if i == 0: # initialize self.best
self.best = particle.position
if particle.eval() < self._eval(): # check if there is a better and if, set it
self.best = copy(particle.position)
self.particles.append(particle) # append the particle to the swarm
def _eval(self):
return self.objective.evaluate(self.best)
def plot(self):
for i in range(self.max_iteration):
pos_x = []
pos_y = []
pos_z = []
#print pos_x
for particle in self.particles:
[x,y,z] = particle.trail[i]
pos_x.append(x)
pos_y.append(y)
pos_z.append(z)
#print pos_x
if i ==0:
g = mlab.points3d(pos_x, pos_y,pos_z, scale_factor=0.5)
ms =g.mlab_source
ms.anti_aliasing_frames = 0
ms.set(x=pos_x, y = pos_y, z = pos_z,scale_factor=0.5) #updating y value
#print pos_y
#ms.set(x=pos_x) # update x values
#ms.set(y=pos_y) #updating y value
#ms.set(z=pos_z) #updating y value
#for p in self.particles:
#p.plot()
def plot_objective(self):
delta = 0.1
v = mgrid[self.min:self.max:delta,self.min:self.max:delta]
z = self.objective.evaluate(v)
#mlab.mesh(v[0],v[1],z)
mlab.surf(v[0],v[1],z) # surf creates a more efficient data structure than mesh
mlab.xlabel('x-axis', object=None)
mlab.ylabel('y-axis', object=None)
mlab.zlabel('z-axis', object=None)
def _info(self):
self.plot()
print '----------------------------'
print 'The best result is:'
print 'Coordinates:', self.best
print 'Value: ', self._eval()
#print 'with ', nreval, 'evaluations'
print 'nr of particles: ', len(self.particles)
print '----------------------------'
def run(self):
self.plot_objective()
self.best = self.particles[0].get_position()
iteration = 0
while iteration < self.max_iteration:
#if iteration!= 0: obj.scene.disable_render = True
#disable_render = True
for particle in self.particles:
rnd_c1 = array([random.uniform(0,1),random.uniform(0,1)])
rnd_c2 = array([random.uniform(0,1),random.uniform(0,1)])
particle.velocity = self.omega * array(particle.velocity) + \
self.c1 * rnd_c1 * (array(particle.best) - array(particle.position)) + \
self.c2 * rnd_c2 * (array(self.best) - array(particle.position)) # TODO: change so independent rnd for components
particle.position = array(particle.position) + particle.velocity
if particle.eval() < particle.best_eval():
particle.best = copy(particle.position)
if particle.eval() < self._eval():
self.best = copy(particle.position)
particle.update() # add the point to the trail
iteration +=1
self.best_evolution.append(self._eval())
#obj.scene.disable_render = False
print 'finished: ', iteration
self._info()
'''
Class modeling particle
'''
class Particle():
def __init__(self, swarm):
self.swarm = swarm
x_rand = random.uniform(self.swarm.min,self.swarm.max)
y_rand = random.uniform(self.swarm.min,self.swarm.max)
self.position = array([x_rand,y_rand])
v_x_rand = random.uniform(self.swarm.min,self.swarm.max)
v_y_rand = random.uniform(self.swarm.min,self.swarm.max)
self.velocity = array([v_x_rand, v_y_rand])
self.size = 0.5
self.best = self.position
# visualization
self.trail = []
def plot(self):
[x,y] = self.position
z = self.eval()
mlab.points3d(x,y,z,scale_factor=self.size)
def eval(self):
return self.swarm.objective.evaluate(self.position)
def best_eval(self):
return self.swarm.objective.evaluate(self.best)
def get_position(self):
return self.position
def update(self):
[x,y] = self.position
z = self.eval()
#print [x,y,z]
self.trail.append([x,y,z])
def plot_trail(self,index):
[x,y,z] = self.trail[index]
mlab.points3d(x,y,z,scale_factor=self.size)
# Make the animation
mlab.figure(1, bgcolor=(0, 0, 0), size=(1300, 700)) # create a new figure with black background and size 1300x700
objective = ac.Ackley() # make an objective function
swarm = pso.Swarm(objective) # create a swarm
nr_of_particles = 25 # nr of particles in swarm
swarm.add_particles(nr_of_particles)
swarm.run()
#swarm.start()
mlab.show()
print '------------------------------------------------------'
print 'Particle Swarm Optimization'
#objective.info()
print 'Objective function to minimize has dimension = ', objective.get_dimension()
print '# of iterations = ', 1000
print '# of particles in swarm = ', nr_of_particles
print '------------------------------------------------------'

In my case, even though I was somewhat able to do what Brandon Rhodes suggested for a mock program (https://stackoverflow.com/questions/16617814/interacting-with-mlab-scene-while-it-is-being-drawn), I could not manage to convert my already existing larger program.
Then I found this link: http://wiki.wxpython.org/LongRunningTasks
So, I just sprinkled a lot of wx.Yield() s inside my loops. This way I did not need to change my program structure, and I am able to interact with the window. I think better ways are explained in the link.

Your problem is that the wx event loop, which runs the Mayavi GUI window and listens for mouse clicking and dragging and responds by moving the scene, is not getting any time to run during your animation because you are keeping Python captive in your loop without ever letting it return control.
Instead of keeping control of the program with a loop of your own, you need to create a wx.Timer class that advances the scene by one frame update, and that then returns control to the wx event loop after scheduling itself again. It will look something like this:
import wx
...
class Animator(wx.Timer):
def Notify(self):
"""When a wx.Timer goes off, it calls its Notify() method."""
if (...the animation is complete...):
return
# Otherwise, update all necessary data to advance one step
# in the animation; you might need to keep a counter or
# other state as an instance variable on `self`
# [DATA UPDATE GOES HERE]
# Schedule ourselves again, giving the wx event loop time to
# process any pending mouse motion.
self.Start(0, oneShot=True) # "in zero milliseconds, call me again!"
I played with slightly higher values like 1 for the number of milliseconds that wx gets to run the UI with, but could not really tell a difference between that and just choosing 0 and having control returned "immediately".