I have a .bin file I acquired from accelerometer sensor. I want to read the data and plot it in python. I don't know how the data is arranged in the file. Basically I want to have the vector (vertical acceleration, forward acceleration, lateral acceleration) as an output.
data = open("left foot.BIN", "rb").read()
I tried opening and reading the file but I didn't understand the output
Related
How can I extract CHIRPS data value in a file (CSV, xlsx, or txt) along with their coordinates using python code?
However, many cloud base processing platforms like "Climate engine" provides the facility to extract values (daily, monthly, or yearly rainfall)in a CSV or Excel file but the problem is that the data do not have coordinates (LAT and Long) for the specified values.
Thus, we can not visualize and conduct any location-based analyzes in any GIS software.
I tried to extract rainfall data from the CHIRPS dataset using code in jupyter notebook, but I could not succeed to extract the values along with their coordinates in the same file.
I need to calculate amplitude of the audio from a video streaming source which is in .asf format in PYTHON. Currently, I have tried to convert it into .wav file and used wave python package but I need to do it in real time. In short, need to perform following steps;
Continously read input video stream
Pre processing the audio signal
Calculate amplitude in given interval
Currentl used wave library of python and read the stored wav format clip, then extracted the amplitude from the wave.readframes() output such that
wf = wave.open()
data = wf.readframes()
amplitude = data[2]
How would i convert a wave file to an image in such a way that i can recover the original wave file from the image, in python please.
I have heard of wav2vec library but its not clear from the documentation how i would convert the vector back into a wave.
ggg=[]
for wav in os.listdir('/content/drive/My Drive/New folder'):
fs, data = wavfile.read(wav)
ggg.append(data)`
I would like to append instead the image as i am creating a data-set to train an algorithm on. After completing training, the algorithm will generate images from the same distribution that another piece of code should convert to a wave file.
I have some files that contains trajectories data.
Some of the data is running some of it is cycling and some of it is walking.
I classified all of them and I want to load them to pandas to be able to classified new trajectories.
I have data that looks similar to that: GPS Trajectories
I can not find the right way to save the data in pandas. I can convert it to any format in csv file but what is the best way to load it to pandas?
I have an mp3 file and I want to basically plot the amplitude spectrum present in that audio sample.
I know that we can do this very easily if we have a wav file. There are lot of python packages available for handling wav file format. However, I do not want to convert the file into wav format then store it and then use it.
What I am trying to achieve is to get the amplitude of an mp3 file directly and even if I have to convert it into wav format, the script should do it on air during runtime without actually storing the file in the database.
I know we can convert the file like follows:
from pydub import AudioSegment
sound = AudioSegment.from_mp3("test.mp3")
sound.export("temp.wav", format="wav")
and it creates the temp.wav which it supposed to but can we just use the content without storing the actual file?
MP3 is encoded wave (+ tags and other stuff). All you need to do is decode it using MP3 decoder. Decoder will give you whole audio data you need for further processing.
How to decode mp3? I am shocked there are so few available tools for Python. Although I found a good one in this question. It's called pydub and I hope I can use a sample snippet from author (I updated it with more info from wiki):
from pydub import AudioSegment
sound = AudioSegment.from_mp3("test.mp3")
# get raw audio data as a bytestring
raw_data = sound.raw_data
# get the frame rate
sample_rate = sound.frame_rate
# get amount of bytes contained in one sample
sample_size = sound.sample_width
# get channels
channels = sound.channels
Note that raw_data is 'on air' at this point ;). Now it's up to you how do you want to use gathered data, but this module seems to give you everything you need.