Develop Reference

Why after calling a function from an imported python file within a file the PyQt app stops responding?

I have created a PyQt5 GUI in my main.py file which is in a main app folder. In the file for the interface, a button initiates a new class called Calculation (in the file entry.py) passing in the values of several inputs on the page and in this class the startCalculation() method is called. In this method, the different variables are passed to methods in imported python files, then the result of those calculation is returned and passed to the next calculation in another python file. These returns are in the form of arrays containing values (for the y axis which is then displayed using numpy and plotly).
When I run the app and click on the button in the main interface, the app starts loading (rainbow animation on Mac) and it says it is not responding. It is not a problem with the class itself as a normal print test works in the startCalculation() method, but the function from the imported file causes this to happen. Also, no errors are given in the terminal.
The following is code in the PyQt interface file (main.py)
from app import entry
def startButton(self):
massaTotale = float(self.lineEdit.text())
dragCoefficient = float(self.lineEdit_5.text())
liftCoefficient = float(self.lineEdit_6.text())
powerAvionics = float(self.lineEdit_3.text())
powerPayload = float(self.lineEdit_4.text())
airSpeed = float(self.lineEdit_8.text())
valoreUnico = float(self.valoreUnicoEdit.text())
engineEfficiency = float(self.lineEdit_9.text())
turbolenceCoeff = float(self.lineEdit_10.text())
dayOfYear = float(self.day_LineEdit.text())
latitude = float(self.latitude_LineEdit.text())
sunsetHourAngle = float(self.sunsetHourAngle_LineEdit.text())
declination = float(self.declination_LineEdit.text())
newCaluclation = entry.Calculation(massaTotale, dragCoefficient, liftCoefficient, powerAvionics, powerPayload, airSpeed, valoreUnico, engineEfficiency, turbolenceCoeff, dayOfYear, latitude, sunsetHourAngle, declination)
newCaluclation.startCalculation()
And this is the code in the class calling the function in the external file
from app.mainFunctions import pLevel
#constructor method
def __init__(self, massaTotale, dragCoefficient, liftCoefficient, powerAvionics, powerPayload, airSpeed, valoreUnico, efficiencyEngine, turbolenceCoeff, dayOfYear, latitude, sunsetHourAngle, declination):
# calculate plevel
self.totM = massaTotale
self.vair = airSpeed
self.cl = liftCoefficient
self.cd = dragCoefficient
self.efficiencyEngine = efficiencyEngine
self.valoreUnico = valoreUnico
self.powerAvionics = powerAvionics
self.powerPayload = powerPayload
self.turbolenceCoeff = turbolenceCoeff
self.day_of_year = dayOfYear
self.latitude = latitude
self.sunset_hour_angle = sunsetHourAngle
self.declination = declination
#starting the calculation
def startCalculation(self):
self.x_values, self.pLevel_values = pLevel.calculate_pLevel(self.valoreUnico, self.cd, self.cl, self.totM)
'''
self.pEngine_values = pEngine.calculate_pEngine(self.x_values, self.pLevel_values, self.efficiencyEngine, self.turbolenceCoeff)
self.pOut_values = pOut.calculate_pOut(self.x_values, self.pEngine_values, self.powerAvionics, self.powerPayload)
self.I_loctime = I_loctime.calculate_I_loctime(self.day_of_year, self.latitude, self.sunset_hour_angle, self.declination)
self.asm_values = area_Solar_Module.calculate_asm(self.x_values, self.pOut_values, self.I_loctime)
'''
The pLevel.py file has the following code in it and should return the array of values to pass to the second function in the entry Calculation class file.
import math
import numpy as np
import plotly as py
import plotly.graph_objs as go
import ipywidgets as widgets
import plotly.io as pio
import sys
sys.dont_write_bytecode = True
py.offline.init_notebook_mode(connected=True)
pio.renderers.default = "browser"
# calculating pLevel
x_values = []
y_values = []
layoutPLevel = go.Layout(
title="pLevel",
yaxis=dict(
title='pLevel'
),
xaxis=dict(
title='Surface Area Wing'
)
)
def calculate_pLevel(valoreUnico, cd, cl, totM):
x_values = []
count = 0
while (count < 5):
x_values.append(count)
count = count + 0.01
y_values = []
iteration = 0
while (iteration < len(x_values)):
x_value = x_values[iteration]
if (x_value == 0):
y_value = 0
y_values.append(y_value)
else:
if (valoreUnico == 0.0):
# nessun dato per valoreUnico dato, utilizza i due valori separati
y_value = firstPart(cd, cl) * math.sqrt(secondPart(x_value, totM))
y_values.append(y_value)
else:
y_value = valoreUnico * \
math.sqrt(secondPart(x_value, totM))
y_values.append(y_value)
iteration = iteration + 1
else:
yNpArray = np.array(y_values)
xNpArray = np.array(x_values)
tracePLevel = go.Scatter(
x=xNpArray,
y=yNpArray,
mode='lines',
name='pLevel',
line=dict(
shape='spline'
)
)
figPLevel = go.Figure(data=[tracePLevel], layout=layoutPLevel)
figPLevel.show()
return x_values, y_values
def firstPart(cd, cl):
return (cd / cl**(3/2))
def secondPart(x_value, totM):
return (2*(totM * 9.81)**3) / (1.225 * x_value)
The structure of the files is as follows:
-app
-- __main__.py
-- entry.py
-- __init__.py
-- mainFunctions
--- pLevel.py
--- __init__.py

The loop for the x_values in the pLevel function was not adding one to the iteration so the loop went on forever. I just did not notice my error.
Instead of being
while (iteration < len(x_values)):
x_value = x_values[iteration]
if (x_value == 0):
y_value = 0
y_values.append(y_value)
It should be
while (iteration < len(x_values)):
x_value = x_values[iteration]
if (x_value == 0):
y_value = 0
y_values.append(y_value)
iteration = iteration+1

How to pause/resume matplotlib ArtistsAnimation

My problem is to resume an matplotlib ArtistsAnimation
My code is:
def PlotFields(self):
fig = plt.figure()
axess = []
for i in range(0,self.number_fields):
axess.append(fig.add_subplot((self.number_fields+1)//2,2,i+1))
#find min and max in time
mins = {}
maxs = {}
for key,field in self.fields.items():
mins[key] = field[:,:,:,self.z_slice].min()
maxs[key] = field[:,:,:,self.z_slice].max()
if mins[key] == maxs[key]:#this fixes bug in imshow when vmin = vmax
mins[key] = mins[key]-0.1
maxs[key] = maxs[key]+0.1
#set up list of images for animation
movie = []
images = []
nt = self.fields[list(self.fields)[0]].shape[0] #number of time slices
print('time slices = {}'.format(nt))
first = 1
for time in range(0,nt):
images.clear()
i = 0
for key,field in self.fields.items():
if self.cut == 'xy':
images.append(axess[i].pcolor(field[time,:,:,self.z_slice].T, vmin = mins[key], vmax = maxs[key]))
axess[i].set_xlabel('x')
axess[i].set_ylabel('y')
elif self.cut == 'xz':
images.append(axess[i].pcolor(field[time,:,:,self.y_slice].T, vmin = mins[key], vmax = maxs[key]))
axess[i].set_xlabel('x')
axess[i].set_ylabel('z')
else:
print('unknown cut --- exiting !!')
quit()
axess[i].set_title(key)
i = i + 1
if first == 1:
for i in range(0,self.number_fields):
fig.colorbar(images[i], ax = axess[i])
first = 0
# time_title.set_text('t={}'.format(t_array[time]))
time_title = axess[0].annotate('t={}'.format(self.t_array[time]),xy = (0.1,1.2))
collected_list = [*images] #apparently a bug in matplotlib forces this solution
collected_list.append(time_title)
movie.append(collected_list)
#for i in range(0,number_fields):
# fig.colorbar(images[i], ax = axess[i])
#run animation
self.ani = anim.ArtistAnimation(fig, movie, interval=500, blit = False, repeat_delay = 1000)
fig.canvas.mpl_connect('button_press_event', self.onClick)
if self.save_movie == True:
try:
ani.save('xy_film.mp4')
#ani.save('film.mp4',writer = FFwriter, fps=30, extra_args=['-vcodec', 'libx264'])
except Exception:
print("Save failed: Check ffmpeg path")
plt.show()
def onClick(self, event):
self.pause == True
if self.pause == False:
self.ani._stop()
self.pause = True
print('self pause = False')
else:
self.ani._start()
self.pause = False
#pause ^= True
print('self pause = True')
The animation stop onClick but throws the following error on the second click which is supposed to resume the animation (if possible):
File "PlotFieldsFieldAligned.py", line 149, in onClick
self.ani._start()
File "/home/yolen/scicomp/anaconda3/lib/python3.5/site- packages/matplotlib/animation.py", line 665, in _start
self.event_source.add_callback(self._step)
AttributeError: 'NoneType' object has no attribute 'add_callback'
Any help appreciated:-D

Although it seems that in this case a FuncAnimation might be better suited than an ArtistAnimation, both can be
stopped / started the same way. See this question stop / start / pause in python matplotlib animation.
The main point is that the 'private' ArtistAnimation._start() function is not doing what you think it does. Therefore it is sensible to use the ArtistAnimation.event_source.stop() and ArtistAnimation.event_source.start() functions.
Below is a minimal, runnable example showing how to start/stop an ArtistAnimation by clicking with the mouse button.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.animation as animation
class SomeClass():
def __init__(self):
self.pause = False
self.fig, ax = plt.subplots()
ax.set_aspect("equal")
self.movie = []
nt = 10
X,Y = np.meshgrid(np.arange(16), np.arange(16))
for t in range(nt):
data = np.sin((X+t*1)/3.)**2+ 1.5*np.cos((Y+t*1)/3.)**2
pc = ax.pcolor(data)
self.movie.append([pc])
self.ani = animation.ArtistAnimation(self.fig, self.movie, interval=100)
self.fig.canvas.mpl_connect('button_press_event', self.onClick)
plt.show()
def onClick(self, event):
if self.pause:
self.ani.event_source.stop()
else:
self.ani.event_source.start()
self.pause ^= True
a = SomeClass()

python real time plot of serial port data has huge lag

I am trying to read from serial port and plot the data in graph using matplot.
Following is my code :
I see that because of plot, there is huge lag (data in queue goes up to 10000 bytes) hence i dont see real time plot coming. Can you please help me if i am doing anything wrong.
<
import serial # import Serial Library
import numpy # Import numpy
import matplotlib.pyplot as plt
Accelerometer= []
COM_read = serial.Serial('COM5', 9600, timeout=None,parity='N',stopbits=1,rtscts=0) #Creating our serial object name
plt.ion() #Tell matplotlib you want interactive mode to plot live data
cnt=0
COM_read.flushInput()
COM_read.flushOutput()
def makeFig(): #Create a function that makes our desired plot
plt.title('My Live Streaming Sensor Data') #Plot the title
plt.grid(True) #Turn the grid on
plt.ylabel('Acc in g') #Set ylabels
plt.plot(Accelerometer, 'ro-', label='Accelerometer g') #plot the temperature
plt.legend(loc='upper left') #plot the legend
plt.ylim(15000,30000) #Set limits of second y axis- adjust to readings you are getting
print "came through"
while True: # While loop that loops forever
print COM_read.inWaiting()
while (COM_read.inWaiting()==0): #Wait here until there is data
pass #do nothing
s = COM_read.readline() #read the line of text from the serial port
decx = int(s[0:4],16)
decy = int(s[5:9],16)
decz = int(s[10:14],16)
if decx > 32768:
decx = decx - 65536;
if decy > 32768:
decy = decy - 65536;
if decz > 32768:
decz = decz - 65536;
#print decx,decy,decz
res = ((decx*decx) + (decy*decy) + (decz*decz))**(1.0/2.0)
Accelerometer.append(res) #Build our Accelerometer array by appending temp readings
drawnow(makeFig) #Call drawnow to update our live graph
plt.pause(.000001) #Pause Briefly. Important to keep drawnow from crashing
cnt=cnt+1
if(cnt>50): #If you have 50 or more points, delete the first one from the array
Accelerometer.pop(0) #This allows us to just see the last 50 data points
>
----------------Based on Dr.John's suggestion, the code is written as follows -----
import serial
import matplotlib
import threading, time
import pylab
import matplotlib.pyplot as plt
import matplotlib.animation as anim
class MAIN:
#ser =0;
def __init__(self):
self.res = 0.0
self.ser = serial.Serial('COM5', 9600, timeout=None,parity='N',stopbits=1,rtscts=0)
self.ser.flushInput()
elf.ser.flushOutput()
c = self.ser.read(1);
while(c != '\n'):
=self.ser.read(1)
return
def acquire_data(self):
s = self.ser.readline()
decx = int(s[0:4],16)
decy = int(s[5:9],16)
decz = int(s[10:14],16)
if decx > 32768:
decx = decx - 65536;
if decy > 32768:
decy = decy - 65536;
if decz > 32768:
decz = decz - 65536;
self.res = ((decx*decx) + (decy*decy) + (decz*decz))**(1.0/2.0)
print self.res
return
ex = MAIN()
threading.Timer(2,ex.acquire_data).start() #starts function acquire_data every 2 sec and resets self.res with 0.5Hz
fig, ax = plt.subplots()
ax.plot(time.time(), self.res, 'o-')
print "closed"

A while true loop is a bad idea in most cases.
Non-OO-Programming is a bad idea in most cases.
Appending plot-data to lists is a bad idea in most cases (laggy).
Start the data aquisition at defined times with
import threading, time
threading.Timer(2, acquire_data).start() #starts function acquire_data every 2 sec and resets self.res with 0.5Hz
then instead of the while-loop, please define class based functions for your own benefit:
class MAIN:
def __init__(self):
self.res = 0
def acquire_data(self):
....
return self.res
and plot with
fig, ax = plt.subplots()
ax.plot(time.time(), self.res, 'o-')
Greets Dr. Cobra

Carrying out unit testing in python on a method that implements ImageDraw

I am currently experimenting with the pytest module to create unit tests for a project I'm working on. I'm trying to test the 'add_point' method which draws an ellipse based on a set of pixels. What I want to do is inspect 'draw' to ensure that the ellipse has been created successfully. Unfortunately I don't know how to go about this, so any help will be appreciated. Here's my code so far:
(A) TheSlicePreviewMaker.py
import os, Image, ImageDraw, ImageFont
from json_importer import json_importer
class SlicePreviewer(object):
def __init__(self):
self.screen_size = (470, 470)
self.background_colour = (86,0,255)
self.platform_fill_colour = (100, 100, 100)
self.platform_outline_colour = (0, 0, 0)
self.platform_window = (0,0,469,469)
self.point_colour = (0,0,255)
self.config_object = json_importer("ConfigFile.txt")
self.image = None
def initialise_image(self):
self.image = Image.new('RGB',self.screen_size,self.background_colour)
draw = ImageDraw.Draw(self.image)
draw.rectangle(self.platform_window,outline=self.platform_outline_colour,fill=self.platform_fill_colour)
del draw
def add_point(self, px, py):
x1 = px - 1
y1 = py - 1
x2 = px + 1
y2 = py + 1
draw = ImageDraw.Draw(self.image)
draw.ellipse((x1,y1,x2,y2),outline=self.point_colour,fill=self.point_colour)
return draw #del draw
def save_image(self, file_name):
self.image.save(file_name, "BMP")
(B) test_TheSlicePreviewMaker.py
from TheSlicePreviewMaker import SlicePreviewer
slice_preview = SlicePreviewer()
class TestSlicePreviewer:
def test_init(self):
'''check that the config file object has been created on init'''
assert slice_preview.config_object != None
def test_initialise_image(self):
'''verify if the image has been successfully initialised'''
assert slice_preview.image.mode == 'RGB'
def test_add_point(self):
'''has the point been drawn successfully?'''
draw = slice_preview.add_point(196,273)
assert something
import pytest
if __name__ == '__main__':
pytest.main("--capture=sys -v")
SN: I've run TheSlicePreviewMaker.py separately to check the bitmap file it produces, so I know that the code works. What I want to achieve is unit test this so that each time I don't have to go check the bitmap.

One approach is to manually inspect the generated image and if looks OK to you, save it next to the test and use a image diffing algorithm (for example ImageChops.difference) to obtain a threshold value that you can use to make sure future test runs are still drawing the same image.
For example:
# contents of conftest.py
from PIL import ImageChops, ImageDraw, Image
import pytest
import os
import py.path
import math
import operator
def rms_diff(im1, im2):
"""Calculate the root-mean-square difference between two images
Taken from: http://snipplr.com/view/757/compare-two-pil-images-in-python/
"""
h1 = im1.histogram()
h2 = im2.histogram()
def mean_sqr(a,b):
if not a:
a = 0.0
if not b:
b = 0.0
return (a-b)**2
return math.sqrt(reduce(operator.add, map(mean_sqr, h1, h2))/(im1.size[0]*im1.size[1]))
class ImageDiff:
"""Fixture used to make sure code that generates images continues to do so
by checking the difference of the genereated image against known good versions.
"""
def __init__(self, request):
self.directory = py.path.local(request.node.fspath.dirname) / request.node.fspath.purebasename
self.expected_name = (request.node.name + '.png')
self.expected_filename = self.directory / self.expected_name
def check(self, im, max_threshold=0.0):
__tracebackhide__ = True
local = py.path.local(os.getcwd()) / self.expected_name
if not self.expected_filename.check(file=1):
msg = '\nExpecting image at %s, but it does not exist.\n'
msg += '-> Generating here: %s'
im.save(str(local))
pytest.fail(msg % (self.expected_filename, local))
else:
expected = Image.open(str(self.expected_filename))
rms_value = rms_diff(im, expected)
if rms_value > max_threshold:
im.save(str(local))
msg = '\nrms_value %s > max_threshold of %s.\n'
msg += 'Obtained image saved at %s'
pytest.fail(msg % (rms_value, max_threshold, str(local)))
#pytest.fixture
def image_diff(request):
return ImageDiff(request)
Now you can use the image_diff fixture in your tests. For example:
def create_image():
""" dummy code that generates an image, simulating some actual code """
im = Image.new('RGB', (100, 100), (0, 0, 0))
draw = ImageDraw.Draw(im)
draw.ellipse((10, 10, 90, 90), outline=(0, 0, 255),
fill=(255, 255, 255))
return im
def test_generated_image(image_diff):
im = create_image()
image_diff.check(im)
The first time your run this test, it will fail with this output:
================================== FAILURES ===================================
____________________________ test_generated_image _____________________________
image_diff = <test_foo.ImageDiff instance at 0x029ED530>
def test_generated_image(image_diff):
im = create_image()
> image_diff.check(im)
E Failed:
E Expecting image at X:\temp\sandbox\img-diff\test_foo\test_generated_image.png, but it does not exist.
E -> Generating here: X:\temp\sandbox\img-diff\test_generated_image.png
You can then manually check the image and if everything is OK, move it to a directory with the same name as the test file, with the name of the test as the file name plus ".png" extension. From now one whenever the test runs, it will check that the image is similar within an acceptable amount.
Suppose you change the code and produce a slightly different image, the test will now fail like this:
================================== FAILURES ===================================
____________________________ test_generated_image _____________________________
image_diff = <test_foo.ImageDiff instance at 0x02A4B788>
def test_generated_image(image_diff):
im = create_image()
> image_diff.check(im)
E Failed:
E rms_value 2.52 > max_threshold of 0.0.
E Obtained image saved at X:\temp\sandbox\img-diff\test_generated_image.png
test_foo.py:63: Failed
========================== 1 failed in 0.03 seconds ===========================
The code needs some polishing but should be a good start. You can find a version of this code here.
Cheers,

Memory allocated to Python is not released back in Linux even after gc.collect()

I have written code within Python that doesn't release memory the way it should. The memory is taken by Python but never gets released even after not being used anymore. Even if you break the running program with ctrl+c. Delete the variable and run gc.collect() it doesn't seem to collect. Or the same as in Ipython and running %reset. The memory won't be freed and running gc.collect() has no effect. I tested this in Windows because I wanted to see if it could possibly be with the garbage collector library. It appears that is the case. Run the code below in Linux and then also in windows. Then compare the memory usage. You will need numpy and scipy installed. Any help or insight on this issue would be much appreciated.
Import the Model, create an instance, and then run createSpecific().
Here is a code that exhibits this behavior in Ubuntu 10.04:
from numpy import array, maximum,intersect1d, meshgrid, std, log, log10, zeros, ones, argwhere, abs, arange, size, copy, sqrt, sin, cos, pi, vstack, hstack, zeros, exp, max, mean, savetxt, loadtxt, minimum, linspace, where
from numpy.fft import fft
from scipy.stats import f_oneway, kruskal, sem, scoreatpercentile
#import matplotlib
#matplotlib.use('cairo.pdf')
from matplotlib.pyplot import plot, clf, show, cla, xlim, xscale, imshow, ylabel, xlabel, figure, savefig, close, bar, title, xticks, yticks, axes, axis
from matplotlib.axes import Axes
from mpl_toolkits.mplot3d import Axes3D
#from enthought.mayavi import mlab
from matplotlib import cm
import matplotlib.pyplot as plt
import os
from time import clock
from timeit import Timer
class Model:
#Constructors and default includes
def __init__(self, prevAud = None, debug=False):
if (prevAud == None):
self.fs=16000. #sample rate
self.lowFreq=60.
self.hiFreq=5000.
self.numFilt=300 #number of channel
self.EarQ = 9.26449 #9.26449
self.minBW = 24.7 #24.7
self.integrationWindow=.01
self.sliceAt=.035
self.maxOverallInhibit = 0.1
self.winLen = int(self.fs*self.integrationWindow+.01) #default integration window 10 ms
self.fullWind = 0.300
self.outShortWindow = None
self.siderArray = None
self.maxNormalizeValue = .284 # Optimized at .284
self.outputSemiModel = None
self.semitones = 11
self.activationTrace = None
return
def setErbScale(self, erbScale = None):
if (erbScale ==None):
self.erbScale = arange(100,500,5)
else:
self.erbScale = erbScale
def trainModel(self,soundVec=None, fs=None, lowfreq=None, highfreq=None, numfilt=None, figto=0, savefig = 'N', prompts=False, plotter=False):
self.setErbScale()
templateArray = self.intWindow(self.halfWaveRec(self.creGammatone(soundVec)))
for i in xrange(templateArray[0].size):
self.outerTest(self.innerTest(templateArray[:,i]))
return templateArray
def createSpecific(self, freqArray = None, semitones = 11, timeforHarm = .3, soundVec=None, fs=None, lowfreq=None, highfreq=None, numfilt=None, figto=0, saveData='N', fileDir='TempRunT/', prompts=False, plotter=False):
if (freqArray == None):
self.setErbScale()
freqArray = self.erbScale
if (type(semitones) == int):
semitones = arange(semitones+1)
totalRuns = int(timeforHarm/self.integrationWindow+.001)
inhibitWindowArray = zeros((freqArray.size,(semitones.size),self.numFilt,totalRuns))
for x in xrange(freqArray.size):
tempHarm = self.makeHarmonicAmpMod(freqArray[x],timeforHarm, numHarm=7,modulation=10)
for y in semitones:
tempChord = self.makeSemiChordAmpMod(tempHarm, freqArray[x],timeforHarm,modulation=10,numHarm=7,semi=y)
inhibitWindowArray[x,y] = self.trainModel( tempChord, savefig = 'N', plotter=plotter)
self.inhibitWindowArray = inhibitWindowArray
def creGammatone(self, soundVec):
temp = zeros((300,soundVec.size))
for i in xrange(temp[:,0].size):
temp[i] = -1**i*soundVec
return temp
def halfWaveRec(self, halfWaveFilts):
filtShape = halfWaveFilts.shape
if (filtShape[1] != int(self.fs*self.fullWind)):
halfWaveFilts = hstack((halfWaveFilts,zeros((self.numFilt,int(self.fs*self.fullWind)-filtShape[1]))))
temp = zeros((halfWaveFilts[:,0].size,halfWaveFilts[0].size))
halfWaveFilts = maximum(halfWaveFilts,temp)
del temp
return halfWaveFilts
def intWindow(self, integratedFilts):
winlen = self.winLen
length = integratedFilts[0].size/winlen
mod = integratedFilts[0].size%winlen
outShortWindow = zeros((integratedFilts[:,0].size,length))
meanval = 0
if (mod != 0):
for i in xrange(integratedFilts[:,0].size):
mean(integratedFilts[i,0:-mod].reshape(length,winlen),1,out=outShortWindow[i])
else:
for i in xrange(integratedFilts[:,0].size):
mean(integratedFilts[i].reshape(length,winlen),1,out=outShortWindow[i])
del integratedFilts
return outShortWindow
def innerTest(self, window):
temper = copy(window)
sider = 7
st = .04
sizer = temper.size
inhibVal = 0
for j in xrange(sider):
inhibVal = (temper[0:j+sider+1].sum())*(sider*2+1)/(sider+1+j)
window[j] += - st*(inhibVal)
for j in xrange(sider,sizer - sider):
inhibVal = temper[j-sider:j+sider+1].sum()
window[j] += - st*(inhibVal)
for j in xrange(sizer-sider, sizer):
inhibVal = (temper[j-sider:sizer].sum())*(sider*2+1)/(sider+sizer-j)
window[j] += - st*(inhibVal)
maxsub = max(window) * self.maxOverallInhibit
window += - maxsub
del temper
return window
def outerTest(self, window):
newSatValue = scoreatpercentile(window, (76))
numones = where(window > newSatValue)
window[numones]=1
self.maxSatValue = newSatValue
del numones
return window
def makeHarmonicAmpMod(self, freq = 100, time = 1.,modulation=10, fsamp=None, numHarm=7):
if fsamp == None: fsamp = self.fs
samples = arange(time*fsamp)
signal = 0
for x in xrange(1,(numHarm+1),1):
signal = signal + sin(samples/float(fsamp)*x*freq*2*pi)
signal = (signal)*maximum(zeros(time*fsamp),sin((samples/float(fsamp)*modulation*2*pi)))
return signal
def makeSemiChordAmpMod(self, harmVec = None, freq=100, time = 1., modulation=10, fsamp=None, numHarm=7, semi = 2):
if (harmVec == None): harmVec = self.makeHarmonicAmpMod(freq,time,modulation,fsamp,numHarm)
if (semi == 0): return harmVec
return harmVec + self.makeHarmonicAmpMod(freq*(2**(semi/12.)),time,modulation,fsamp,numHarm)

Virtual memory is not a scarce resource. There is no need to return it to the system since each process has its own address space. What is your actual problem? What issue is this behavior causing you?

I had installed the latest svn of numpy and the issue had vanished. I assume it was inside one of the numpy functions. I never got a chance to dig further into it.