My code worked, I changed nothing, and now its highlighted saying it cant reference the module properly
ive tried retyping everything, looking up tutorials, trying SUPER simple lines of code, and my random module in pycharm python 3 is not working
import random
value = random.random()
print(value)
basically,
value = random.random() after the dot is highlighted and is saying cannot find reference 'random' in random, like... what??
If there is a space after the dot(random. random() ) remove it.
random.random()
Try it
Related
I am using PyObjC bindings to try to get a spoken sound file from phonemes.
I figured out that I can turn speech into sound as follows:
import AppKit
ss = AppKit.NSSpeechSynthesizer.alloc().init()
ss.setVoice_('com.apple.speech.synthesis.voice.Alex')
ss.startSpeakingString_toURL_("Hello", AppKit.NSURL.fileURLWithPath_("hello.aiff"))
# then wait until ve.isSpeaking() returns False
Next for greater control I'd like to turn the text first into phonemes, and then speak them.
phonemes = ss.phonemesFromText_("Hello")
But now I'm stuck, because I know from the docs that to get startSpeakingString to accept phonemes as input, you first need to set NSSpeechSynthesizer.SpeechPropertyKey.Mode to "phoneme". And I think I'm supposed to use setObject_forProperty_error_ to set that.
There are two things I don't understand:
Where is NSSpeechSynthesizer.SpeechPropertyKey.Mode in PyObjC? I grepped the entire PyObjC directory and SpeechPropertyKey is not mentioned anywhere.
How do I use setObject_forProperty_error_ to set it? I think based on the docs that the first argument is the value to set (although it's called just "an object", so True in this case?), and the second is the key (would be phoneme in this case?), and finally there is an error callback. But I'm not sure how I'd pass those arguments in Python.
Where is NSSpeechSynthesizer.SpeechPropertyKey.Mode in PyObjC?
Nowhere.
How do I use setObject_forProperty_error_ to set it?
ss.setObject_forProperty_error_("PHON", "inpt", None)
"PHON" is the same as NSSpeechSynthesizer.SpeechPropertyKey.Mode.phoneme
"inpt" is the same as NSSpeechSynthesizer.SpeechPropertyKey.inputMode
It seems these are not defined anywhere in PyObjC, but I found them by firing up XCode and writing a short Swift snippet:
import Foundation
import AppKit
let synth = NSSpeechSynthesizer()
let x = NSSpeechSynthesizer.SpeechPropertyKey.Mode.phoneme
let y = NSSpeechSynthesizer.SpeechPropertyKey.inputMode
Now looking at x and y in the debugger show that they are the strings mentioned above.
As for how to call setObject_forProperty_error_, I simply tried passing in those strings and None as the error handler, and that worked.
I'm currently using a python module called petsc4py (https://pypi.org/project/petsc4py/). My main issue is that none of the typical intellisense features seems to work with this module.
I'm guessing it might have something to do with it being a C extension module, but I am not sure exactly why this happens. I initially thought that intellisense was unable to look inside ".so" files, but it seems that numpy is able to do this with the array object, which in my case is inside a file called multiarray.cpython-37m-x86_64-linux-gnu (check example below).
Does anyone know why I see this behaviour in the petsc4py module. Is there anything that I (or the developers of petsc4py) can do to get intellisense to work?
Example:
import sys
import petsc4py
petsc4py.init(sys.argv)
from petsc4py import PETSc
x_p = PETSc.Vec().create()
x_p.setSizes(10)
x_p.setFromOptions()
u_p = x_p.duplicate()
import numpy as np
x_n = np.array([1,2,3])
u_n = x_n.copy()
In this example, when trying to work with a Vec object from petsc4py, doing u_p.duplicate() cannot find the function and the suggestion is simply a repetition of the function immediately before. However, using an array from numpy, doing u_n.copy() works perfectly.
If you're compiling in-place then you're bumping up against https://github.com/microsoft/python-language-server/issues/197.
I have the following code in a function:
phi = fourier_matrix(y, fs)
N = np.size(phi,axis=1)
x = np.ones(N)
for i in range(136):
W_pk = np.diag(x)
temp = pinv(np.dot(phi, W_pk))
q_k = np.dot(temp, y)
x = np.dot(W_pk, q_k)
where phi is (96,750), W_pk is (750,750) and q_k is (750,).
This throws the following error:
Python(12001,0x7fff99d8b380) malloc: * error for object 0x7fc71aa37200: incorrect checksum for freed object - object was probably modified after being freed.
* set a breakpoint in malloc_error_break to debug
If I comment the last dot product the error does not appear.
I think I need to free memory in some way or maybe do the dot product in a different way?
Also, this only happens when I run it from a mac. On windows or linux it does not throw the error.
Python is 3.6 (tried with 3.7), and numpy is 1.14.5, also tried with 1.15
Any help would be greatly appreciated, since I really need to make this work!
Thanks in advance.
EDIT I:
I tried this portion of the code on a jupyter notebook, and it didn't fail. This confused me even more! It fails when I run it in Visual Studio Code on a mac. The rest of my code, an algorithm to remove artifacts from a signal, works as it should until I add that last piece of code x = np.dot(W_pk, q_k). Maybe it works on jupyter because I don't run the rest of the algorithm there? but as I said, it only crashes on that last dot product.
EDIT II: I added the piece of code above the for loop to this question, because I found that the problem is somehow related to how x is being used. You see it's declared above as a float64 ndarray. When it reaches the last line of the for loop, the dot product returns a complex128 (should be complex64, don't know what's happening there) and overwrites the x array. The first time works, but the second time it crashes when trying to overwrite. If I use a new variable for the dot product, say z, then it does not crash! not sure why... but I need to overwrite x in each iteration.
Furthermore, if I do something like this:
z = np.dot(W_pk, q_k)
x = abs(z) #I don't need complex numbers at this point
Then it crashes with the same error on the first dot product (presumably):
temp = pinv(np.dot(phi, W_pk))
Also, the memory consumption is not that bad, around 110M according some measurements, and the same algorithm does not crash on iPython with twice the memory usage. This is what I find the most obscure, why doesn't it crash on iPython??
I was able to successfully install matplotlib; however, when I run my code, I don't get an error, but instead the Python icon bouncing over and over again.
I don't know if it has anything to do with
from rcParamsSettings import *
I don't have any idea what that is, but if I try to run it with that line uncommented out, I get
ImportError: No module named rcParamsSettings
Here's the code I'm trying to run:
import pylab
#from rcParamsSettings import *
import random
def flipPlot(minExp, maxExp):
"""Assumes minExp and maxExp positive integers; minExp < maxExp
Plots results of 2**minExp to 2**maxExp coin flips"""
ratios = []
diffs = []
xAxis = []
for exp in range(minExp, maxExp + 1):
xAxis.append(2**exp)
for numFlips in xAxis:
numHeads = 0
for n in range(numFlips):
if random.random() < 0.5:
numHeads += 1
numTails = numFlips - numHeads
ratios.append(numHeads/float(numTails))
diffs.append(abs(numHeads - numTails))
pylab.title('Difference Between Heads and Tails')
pylab.xlabel('Number of Flips')
pylab.ylabel('Abs(#Heads - #Tails)')
pylab.plot(xAxis, diffs)
pylab.figure()
pylab.title('Heads/Tails Ratios')
pylab.xlabel('Number of Flips')
pylab.ylabel('#Heads/#Tails')
pylab.plot(xAxis, ratios)
random.seed(0)
flipPlot(4, 20)
What do I need to do to get the code running?
Note: My experience with programming and Python is very limited; I'm just starting out.
You need to use pylab.show() otherwise nothing will come up.
See the example here: http://matplotlib.org/examples/pylab_examples/simple_plot.html
(although you should avoid using from pylab import *, the way you import is fine.)
There are cases in MatPlotLib where you do not want to show the graph. An example I have is designing a graph on a server using data collected by an automated process. The server has no screen and is only accessed by a terminal, and therefore trying to show a graph will result in an error as there is no DISPLAY variable set (there is no screen to display to).
In this case it would be normal to save the figure somewhere using pylab.savefig(), and then maybe email it or use an ftp to send it somewhere it can be viewed.
Because of this MatPlotLib will not implicitly show a graph and must be asked to show it.
Your code works for me if I add a pylab.show().
Alternatively, you could save the plot to a file:
pylab.savefig("file.png")
This makes sense if you are updating your plot frequently.
I'm doing simulations for scientific computing, and I'm almost always going to want to be in the interactive interpreter to poke around at the output of my simulations. I'm trying to write classes to define simulated objects (neural populations) and I'd like to formalize my testing of these classes by calling a script %run test_class_WC.py in ipython. Since the module/file containing the class is changing as I try to debug it/add features, I'm reloading it each time.
./test_class_WC.py:
import WC_class # make sure WC_class exists
reload(WC_class) # make sure it's the most current version
import numpy as np
from WC_class import WC_unit # put the class into my global namespace?
E1 = WC_unit(Iapp=100)
E1.update() # see if it works
print E1.r
So right off the bat I'm using reload to make sure I've got the most current version of the module loaded so I've got the freshest class definition-- I'm sure this is clunky as heck (and maybe more sinister?), but it saves me some trouble from doing %run WC_class.py and having to do a separate call to %run test_WC.py
and ./WC_class:
class WC_unit:
nUnits = 0
def __init__(self,**kwargs):
self.__dict__.update(dict( # a bunch of params
gee = .6, # i need to be able to change
ke=.1,the=.2, # in test_class_WC.py
tau=100.,dt=.1,r=0.,Iapp=1.), **kwargs)
WC_unit.nUnits +=1
def update(self):
def f(x,k=self.ke,th=self.the): # a function i define inside a method
return 1/(1+np.exp(-(x-th)/k)) # using some of those params
x = self.Iapp + self.gee * self.r
self.r += self.dt/self.tau * (-self.r + f(x))
WC_unit basically defines a bunch of default parameters and defines an ODE that updates using basic Euler integration. I expect that test_class_WC sets up a global namespace containing np (and WC_unit, and WC_class)
When I run it, I get the following error:
In [14]: %run test_class_WC.py
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/Users/steeles/Desktop/science/WC_sequence/test_class_WC.py in <module>()
8
9 E1 = WC_unit(Iapp=100)
---> 10 E1.update()
11
12 # if bPlot:
/Users/steeles/Desktop/science/WC_sequence/WC_class.py in update(self)
19 return 1/(1+np.exp(-(x-th)/k))
20 x = self.Iapp + self.gee * self.r
---> 21 self.r += self.dt/self.tau * (-self.r + f(x))
22
23 # #class_method
/Users/steeles/Desktop/science/WC_sequence/WC_class.py in f(x, k, th)
17 def update(self):
18 def f(x,k=self.ke,th=self.the):
---> 19 return 1/(1+np.exp(-(x-th)/k))
20 x = self.Iapp + self.gee * self.r
21 self.r += self.dt/self.tau * (-self.r + f(x))
NameError: global name 'np' is not defined
Now I can get around this by just importing numpy as np in top of the WC_class module, or even by doing from numpy import exp in test_class_WC and change the update() method to contain exp() instead of np.exp()... but I'm not trying to do this because it's easy, I want to learn how all this namespace/module stuff works so I stop being a python idiot. Why is np getting lost in the WC_unit namespace? Is it because I'm dealing with two different files/modules? Does the call to np.exp inside a function have to do with it?
I'm also open to suggestions regarding improving my workflow and file structure, as it seems to be not particularly pythonic. My background is in MATLAB if that helps anyone understand. I'm editing my .py files in SublimeText2. Sorry the code is not very minimal, I've been having a hard time reproducing the problem.
The correct approach is to do an import numpy as np at the top of your sub-module as well. Here's why:
The key thing to note is that in Python, global actually means "shared at a module-level", and the namespaces for each module exist distinct from each other except when a module explicitly imports from another module. An imported module definitely cannot reach out to its 'parent' module's namespace, which is probably a good thing all things considered, otherwise you'll have modules whose behavior depends entirely on the variables defined in the module that imports it.
So when the stack trace says global name 'np' is not defined, it's talking about it at a module level. Python does not let the WC_Class module access objects in its parent module by default.
(As an aside, effbot has a quick note on how to do inter-module globals)
Another key thing to note is that even if you have multiple import numpy as np in various modules of your code, the module actually only gets loaded (i.e. executed) once. Once loaded, modules (being Python objects themselves) can be found in the dictionary sys.modules, and if a module already exists in this dictionary, any import module_to_import statement simply lets the importing module access names in the namespace of module_to_import. So having import numpy as np scattered across multiple modules in your codebase isn't wasteful.
Edit: On deeper digging, effbot has an even deeper (but still pretty quick and simple) exploration of what actually happens in module imports. For deeper exploration of the topic, you may want to check the import system discussion newly added in the Python 3 documentation.
It is normal in Python to import each module that is needed with in each. Don't count on any 'global' imports. In fact there isn't such a thing. With one exception. I discovered in
Do I have to specify import when Python script is being run in Ipython?
that %run -i myscript runs the script in the Ipython interactive namespace. So for quick test scripts this can save a bunch of imports.
I don't see the need for this triple import
import WC_class # make sure WC_class exists
reload(WC_class) # make sure it's the most current version
...
from WC_class import WC_unit
If all you are using from WC_class just use the last line.