I am working on a project,in which I need to extract data from the device: InertialUnit.
I get a single value in real time, but I need data for the first 10 s and in 1 ms increments, or all the data for the entire cycle of the device. Please help me implement this if possible.
Webots controllers are like any other programs, so you can easily get the values of the inertial unit and save them in a file at each step.
Here is a very simple example in Python:
from controller import Robot
robot = Robot()
inertial_unit = robot.getInertialUnit('inertial unit')
inertial_unit.enable(10)
while robot.step(10) != -1:
values = inertial_unit.getValues()
with open('values.txt','a') as f:
f.write('\n'.join(values))
Related
I am currently writing an (simple) analytisis code to sum time connected powerreadings. With the data being assumingly raw (e.g. disturbances from the measuring device have not been calculated out) I have to account for disturbances by calculation the mean of the first one thousand samples. The calculation of the mean itself is not a problem. I only am unsure of how to generate the appropriate DataSet.
For now it looks about like this:
DataSet<Tupel2<long,double>>Gyrotron_1=ECRH.includeFields('11000000000'); // obviously the line to declare the first gyrotron, continues for the next ten lines, assuming separattion of not occupied space
DataSet<Tupel2<long,double>>Gyrotron_2=ECRH.includeFields('10100000000');
DataSet<Tupel2<long,double>>Gyrotron_3=ECRH.includeFields('10010000000');
DataSet<Tupel2<long,double>>Gyrotron_4=ECRH.includeFields('10001000000');
DataSet<Tupel2<long,double>>Gyrotron_5=ECRH.includeFields('10000100000');
DataSet<Tupel2<long,double>>Gyrotron_6=ECRH.includeFields('10000010000');
DataSet<Tupel2<long,double>>Gyrotron_7=ECRH.includeFields('10000001000');
DataSet<Tupel2<long,double>>Gyrotron_8=ECRH.includeFields('10000000100');
DataSet<Tupel2<long,double>>Gyrotron_9=ECRH.includeFields('10000000010');
DataSet<Tupel2<long,double>>Gyrotron_10=ECRH.includeFields('10000000001');
for (int=1,i<=10;i++) {
DataSet<double> offset=Gyroton_'+i+'.groupBy(1).first(1000).sum()/1000;
}
It's the part in the for-loop I'm unsure of. Does anybody know if it is possible to append values to DataSets and if so how?
In case of doubt, I could always put the values into an array but I do not know if that is the wise thing to do.
This code will not work for many reasons. I'd recommend looking into the fundamentals of Java and the basic data structures and also in Flink.
It's really hard to understand what you actually try to achieve but this is the closest that I came up with
String[] codes = { "11000000000", ..., "10000000001" };
DataSet<Tuple2<Long, Double>> result = env.fromElements();
for (final String code : codes) {
DataSet<Tuple2<Long, Double>> codeResult = ECRH.includeFields(code)
.groupBy(1)
.first(1000)
.sum(0)
.map(sum -> new Tuple2<>(sum.f0, sum.f1 / 1000d));
result = codeResult.union(result);
}
result.print();
But please take the time and understand the basics before delving deeper. I also recommend to use an IDE like IntelliJ that would point to at least 6 issues in your code.
I am logging data using IronPython (sensor OEM requires this), and writing that collected data to a .txt file using the first snippet below. My goal is to make real-time calculations on the collected data (like a running window variance), and plot it in real-time. I am reading the .txt file into a script I've written in Spyder.
I am losing about 3/4 of the data points somewhere between the data collection in my first script, and the data read in my Spyder script for the math calculation and visualization.
I am looking for some feedback for the way I am writing my data, and reading it; and if I am risking losing data somewhere along this way.
file = open("C:\\Users\Documents\\test4.txt", "a")
writer = csv.writer(file)
while True:
writer.writerow(myFunction())
file.flush()
In Spyder, I am reading in this file like the following:
def animate(i):
data = pd.read_csv("C:\\Users\Documents\\test4.txt")
data.columns = ['X', 'Y', 'Z']
I am learning machine learning using TensorFlow. I have been through a couple of tutorials but I still have a hard time trying to find what are the good ways of training a model. Recently I implemented a CNN model I found in the litterature. The model must take a crop of a certain size centered on a given pixel and predict the label of this pixel. It does that for each pixel of the image. I used:
classifier = tf.learn.Estimator(model_fn=cnn_model_fn, model_dir="./cnn")
with cnn_model_fn beeing a function I implemented.
For each training image, we take 3000 crops randomly, so I can't load all theses images and their crops to memory. The way I found is by loading one image at a time, extract the 3000 crops and then call classifier.fit() to train on the 3000 crops. Then loop for each image in my dataset.
for i in range(len(filenames)):
...
image = misc.imread(filenames[i])
labels = misc.imread(groundTruth[i]) #labels for each pixels
input_classifier = preprocess(image,...) #crops 3000 images in image and do other things
input_labels = preprocess_labels(labels, ...) #take the corresponding 3000 labels
classifier.fit(x = input_classifier,
y = input_labels,
batch_size = 30
steps = 100)
It worked fine for 100 images, but if I try on the whole dataset (2000 images), it always stops and give an error of ResourceExhausted.
...
[everything goes well]
...
iteration :227/2000
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus
id: 0000:01:00.0)
INFO:tensorflow:Create CheckpointSaverHook.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus
id: 0000:01:00.0)
Traceback (most recent call last):
File "train-cnn.py", line 78, in <module>
classifier.fit(x= input_classifier, y=input_labels,batch_size=30, steps=100)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 280, in new_func
...
...
...
tensorflow.python.framework.errors_impl.ResourceExhaustedError: cnn/graph.pbtxt.tmp32bcc6311c164c29b91177d17d05d669
I don't see why it gets OOM... I have suspicions that it is because of the way I call fit() in loop. After each fit(), a ckpt is saved and it must be restored right after to train on the next image. So is it a bad way to train a model?
running estimator.fit in a loop with smaller steps is not a good idea. I would put all input logic into an input_fn. then run estimator.fit only once with more steps.
An example of reading data from different files can be found here: tf.contrib.learn.read_batch_examples
The issue is a bit complicated so I need to give you the full story:
I'm trying to create basic sound using the package pyaudio.
The pyaudio module asks for a callback function that reads audio data.
It basically looks like this:
def callback():
return sound.readframes()
I have a class that provides audio data. It creates a signal by indexing a numpy array that contains a wave length of a basic wave form. It basically looks like this:
class Wave(object):
data = [] # one wavelength of a basic waveform like a sine wave will be stored here
def readframes(self):
indices = # the indices for the next frames
# based on a frequency and the current position
return self.data[indices]
class SineWave(Wave):
data = # points of a sine wave in a numpy array
sound = SineWave()
# pyaudio now reads audio data in a non-blocking callback mode
Now to the problem:
There was noise between each call to readframes or each block of audio data. The sound itself was still intact, but there was an additional clicking. Increasing the size of the returned data block also increased the time between each click. I still don't quite know what caused the noise, but I fixed it by storing the returned numpy array in the instance instead of returning it directly.
This causes noise:
return self.data[indices]
This does not cause noise:
+ self.result = self.data[indices]
+ return self.result
It can't be an alteration of the data that causes the noise or else this wouldn't fix it. Why does the result have to be stored in the instance?
Update:
I also have to mention that the noise doesn't occur everytime I run the script. It occurs randomly about 50% of the runs. Either the noise appears right at the start and won't go away or it doesn't appear at all.
It looks like an issue with the initialization of the sound device, but how that could be related to my fix is a mistery.
Here is code that reproduces the noise problem:
import pyaudio
import time
from numpy import sin, linspace, arange, pi, float32, uint32
SR = 44100 # sampling rate
FPB = 2048 # frames per buffer
class SineWave(object):
"""A sine wave"""
data_size = 2**10
data = sin(linspace(0, 2*pi, data_size, endpoint=False, dtype=float32))
def __init__(self, frequency):
self.pos = 0
self.fq = frequency
self.ppf = self.data_size * self.fq / SR # points per frame
def readframes(self):
size = FPB
pos = self.pos
indices = (arange(pos, size+pos)*self.ppf).astype(uint32) & (self.data_size-1)
self.pos = (self.pos + size) % SR
self.result = self.data[indices]
return self.data[indices]
def callback(in_data, frame_count, time_info, status):
return sound.readframes(), pyaudio.paContinue
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(4),
channels=1,
rate=SR,
output=True,
stream_callback=callback,
frames_per_buffer=FPB)
sound = SineWave(440)
stream.start_stream()
time.sleep(2)
stream.stop_stream()
stream.close()
p.terminate()
On my system the noise appears in about 90% of the runs.
Changing
return self.data[indices]
to
return self.result
fixes it.
There shouldn't be a difference whether you store the intermediate array in the instance or not. I suspect there is some other problem going on that causes the error you're experiencing. Can you provide code that actually runs and exhibits the problem?
If you want to see a (hopefully!) working implementation, have a look at the play() function of the sounddevice module, especially at the loop argument. With this, you should be able to play your waveform repeatedly.
The only thing you need to store in the instance, is the frame index where you left off, just to continue with the following frame index in the next callback (and of course wrap around in the end). You should also consider the case where your wave form is shorter than an audio block. It might even be much shorter. I solved that by using a recursive function call, see the code.
UPDATE:
It's indeed quite strange that assigning to self "solves" your problem. I think this has something to do with how PyAudio interprets the returned array as Python buffer.
You should rewrite your code using the sounddevice module, which properly supports NumPy arrays and has a sane API for the callback function.
It's easy, just use this callback function:
def callback(outdata, frames, time, status):
if status:
print(status)
outdata[:, 0] = sound.readframes()
And start the stream with:
import sounddevice as sd
stream = sd.OutputStream(
channels=1, samplerate=SR, callback=callback, blocksize=FPB)
Apart from that, your code is flawed since it doesn't do any interpolation when reading from the wavetable.
New to the forum, thanks in advance for any help you could provide.
I have a series of .jpgs that are being presented to users as they study the info contained within. Instructions state that each jpg can be studied for a max of 120secs. I've already coded it such that the jpg will advance after the 120sec limit:
RespKey= []
RT = []
event.clearEvents()
myClock.reset()
t1example = myClock.getTime()
t2example = t1example
while t2example < (t1example+120): # value added to t1 here is timeout value;
RespKey = event.getKeys(keyList=["space"], timeStamped=myClock) # they are told to press space bar when done studying
if len(RespKey) > 0:
RT = RespKey[0][1]
Resp = RespKey[0][0].lower()
print Resp
print RT
break
else:
t2study = myClock.getTime() # end of timeout loop
myWin.flip()
The problem is, I don't know how to make the Clock/ Timer/ Stopwatch function visible to the user while studying the jpg. Is there a way to superimpose a visible clock onto the stimulus so nobody is surprised when the study time comes to an end?
Note: New to coding, please couch jargon in layman speak if at all possible.
Thank you!
Yes. Before the loop (and before re-setting the clock), create a text stimulus like this:
clockText = visual.TextStim(myWin) # include other parameters if you want to change the default size, font, etc
Then on every frame, you will update the content of that stimulus. i.e. just prior to the myWin.flip() call, do this:
clockText.setText(str(t2study)) # you can format/round this as required
clockText.draw()
Check the face_jpg.py Coder demo for an example of displaying text like this on every frame.