I'm trying to read data from this barometric pressure sensor on a raspberry pi using python & i2c/smbus.
The sensor's data sheet (page 10) says it will output a digital value in the range 0-16383 (2**14). So far it seems like I have to read whole bytes, so I'm not sure how to get a 14 bit value. (I had a link to the data sheet, but SO says I need more reputation before I can add more links to posts.)
This sample uses Adafruit's I2C python library, which is basically a wrapper around SMBus.
import Adafruit_I2C
import time
# sensor returns a 14-bit reading
max_output = 2**14
# per data sheet, max_output == 1.6 bar
max_bar = 1.6
# i2c address specified in data sheet
sensor = Adafruit_I2C.Adafruit_I2C(0x78)
while True:
reading = sensor.readU16(0, little_endian=False)
# reading is sometimes, but not always, greater than 2**14
# this adjustment feels pretty hacky/wrong
while reading > max_output:
reading = reading >> 1
bar = reading / float(max_output) * max_bar
print bar
time.sleep(1)
I compare these readings to the output from my handheld GPS, which includes a barometer. I sometimes get readings which are somewhat close (1030 millibar when the GPS reads 1001 millibar), but the sensor then dips drastically (down to 930 millibar) for a few readings. I have a suspicion that this is due to how I'm reading the data, but no real evidence to back that up.
At this point, I'm not sure what to try next.
Some things I've guessed at, but would appreciate some more-informed help with:
How can I read just the 14 bits that the sensor is outputting?
What endian-ness are the returned values? Assuming big-endian produced values which seemed more sane, but I may be conflating multiple problems here.
How can I tell which register to read from? This isn't mentioned in the data-sheet anywhere. I guessed that register 0 is probably the only one.
You should be masking the output of the sensor, not shifting it. e.g. reading = reading & (max_output-1) should probably do it.
The top two bits are the status bits, so if they are set sometimes they could mean things like: normal mode or stale data indicator.
Related
I'm trying to use Python to create a live music visualization. The libraries I'm using are SoundCard (for live audio capture) and Librosa (for short-time Fourier transform).
However I suspect I'm not interpreting the audio data correctly. Looking at the 100Hz-200Hz bin, I get a constant stream of sound even when the song doesn't contain that much bass (or really, any whatsoever). I admit I am a bit in over my head with all the audio processing FFT stuff, since it's not really my expertise and the math beats me most of the time.
This is the function that captures and analyses the audio. lb is set to the speakers and works properly. Fs is set to 48000 and I record 1000 frames in the attempt of keeping 48FPS. fftwindowsize is set to 2048*8 because... I'm not sure. I increased the number until Librosa stopped throwing warnings.
def audioanalysis():
with lb.recorder(samplerate=Fs) as mic:
rawdata = mic.record(numframes=1000)
datalen: int = int(rawdata.size/2)
monodata = numpy.empty(datalen)
for x in range(0, datalen):
monodata[x] = max(rawdata[x][0], rawdata[x][1])
data = numpy.abs(librosa.stft(monodata, n_fft=fftwindowsize, hop_length=1024))
return librosa.amplitude_to_db(data, ref=numpy.max)
And the code for making buckets:
frequencies = librosa.core.fft_frequencies(n_fft=fftwindowsize)
freq_index_ratio = len(frequencies)/frequencies[len(frequencies)-1] / 2
[...]
for i in range(0,buckets):
avg = 0
for j in range (i * bucketsize, (i+1)*bucketsize):
avg += amp(spectrogram=spectrogram, freq=j)
amps[i] = avg/bucketsize
def amp(spectrogram, freq) -> float:
return spectrogram[int(freq*freq_index_ratio)]
Over the course of a song, amps[1] (so 100Hz-200Hz) stays in the -50dB to -30dB range, which isn't really useful or representative of the song playing.
Is my FFT analysis wrong? Is there no way to better interpret short samples of sound?
P.S. I know my Python code isn't excellent. This is my first project in Python :)
I'm using pyaudio to take input from a microphone or read a wav file, and analyze the stream while playing it. I want to only analyze the right channel if the input is stereo. I've been able to extract the data and convert to integers using loops:
levels = []
length = len(data)
if channels == 1:
for i in range(length//2):
volume = abs(struct.unpack('<h', data[i:i+2])[0])
levels.append(volume)
elif channels == 2:
for i in range(length//4):
j = 4 * i + 2
volume = abs(struct.unpack('<h', data[j:j+2])[0])
levels.append(volume)
I think this working correctly, I know it runs without error on a laptop and Raspberry Pi 3, but it appears to consume too much time to run on a Raspberry Pi Zero when simultaneously streaming the output to a speaker. I figure that eliminating the loop and using numpy may help. I assume I need to use np.ndarray to do this, and the first parameter will be (CHUNK,) where CHUNK is my chunk size for analyzing the audio (I'm using 1024). And the format would be '<h', as in the struct code above, I think. But I'm at a loss as to how to code it correctly for each of the two cases (mono and right channel only for stereo). How do I create the numpy arrays for each of the two cases?
You are here reading 16-bit integers from a binary file. It seems that you are first reading the data into data variable with something like data = f.read(), which is here not visible. Then you do:
for i in range(length//2):
volume = abs(struct.unpack('<h', data[i:i+2])[0])
levels.append(volume)
BTW, that code is wrong, it shoud be abs(struct.unpack('<h', data[2*i:2*i+2])[0]), otherwise you are overlapping bytes from different values.
To do the same with numpy, you should just do this (instead of both f.read()and the whole loop):
data = np.fromfile(f, dtype='<i2')
This is over 100 times faster than the manual thing above in my test on 5 MB of data.
In the second case, you have interleaved left-right-left-right values. Again you can read them all (assuming you have enough memory) and then access only one half:
data = np.fromfile(f, dtype='<i2')
left = data[::2]
right = data[1::2]
This processes everything, even though you need just one half, but it is still much much faster.
EDIT: If the data not coming from a file, np.fromfile can be replaced with np.frombuffer. Then you have this:
channel_data = np.frombuffer(data, dtype='<i2')
if channels == 2:
channel_data = channel_data[1::2]
levels = np.abs(channel_data)
Using an ESP32 I am attempting to collect data from my HW-390 Capacitive soil moisture sensor.
However when I run the code on the ESP32 it doesn't give any values.
>>> import pythonsoil6
>>>
I think I need to add a print command but I don't know exactly what I need to do. Could someone look over my code and tell me what the problem is. PS: Written on a raspberry pi using the Thonny IDE.
from machine import ADC, Pin
adc = ADC(Pin(32)) # create ADC object on ADC pin
adc.read() # read value, 0-4095 across voltage range 0.0v - 1.0v
adc.atten(ADC.ATTN_11DB) # set 11dB input attenuation (voltage range roughly 0.0v - 3.6v)
adc.width(ADC.WIDTH_9BIT) # set 9 bit return values (returned range 0-511)
adc.read()
What shall be evaluated and achieved:
I try to record audio data with a minimum of influence by hard- and especially software. After using Adobe Audition for some time I stumbled across PyAudio and was driven by curiosity as well as the possibility to refresh my Python knowledge.
As the fact displayed in the headline above may have given away I compared the sample values of two wave files (indeed sections of them) and had to find out that both programmes produce different output.
As I am definitely at my wit`s end, I do hope to find someone who could help me.
What has been done so far:
An M-Audio “M-Track Two-Channel USB Interface” has been used to record Audio Data with Audition CS6 and PyAudio simultaneously as the following steps are executed in the given order…
Audition is prepared for recording by opening “Prefrences/ Audio Hardware” and selecting the audio interface, a sample rate of 48 kHz and a latency of 250 ms (this value has been examined thoughout the last years as to be the second lowest I can get without getting the warning for lost samples – if I understood the purpose correctly I just have to worry about loosing samples cause monitoring is not an issue).
A new file with one channel, a sample rate of 48 kHz and a bit depth of 24 bit is opened.
The Python code (displayed below) is started and leads to a countdown being used to change over to Audition and start the recording 10 s before Python starts its.)
Wait until Python prints the “end of programme” message.
Stop and save the data recorded by Audition.
Now data has to be examined:
Both files (one recorded by Audition and Python respectively) are opened in Audition (Multitrack session). As Audition was started and terminated manually the two files have completely different beginning and ending times. Then they are aligned visually so that small extracts (which visually – by waveform shape – contain the same data) can be cut out and saved.
A Python programme has been written opening, reading and displaying the sample values using the default wave module and matplotlib.pyplot respectively (graphs are shown below).
Differences in both waveforms and a big question mark are revealed…
Does anybody have an idea why Audition is showing different sample values and specifically where precisely the mistake (is there any?) hides??
some (interesting) observations
a) When calling the pyaudio.PyAudio().get_default_input_device_info() method the default sample rate is listed as 44,1 kHz even though the default M-Track sample rate is said to be 48 kHz by its specifications (indeed Audition recognizes the 48 kHz by resampling incoming data if another rate was selected). Any ideas why and how to change this?
b) Aligning both files using the beginning of the sequence covered by PyAudio and checking whether they are still “in phase” at the end reveals no – PyAudio is shorter and seems to have lost samples (even though no exception was raised and the “exception on overflow” argument is “True”)
c) Using the “frames_per_buffer” keyword in the stream open method I was unable to align both files, having no idea where Python got its data from.
d) Using the “.get_default_input_device_info()” method and trying different sample rates (22,05 k, 44,1 k, 48 k, 192 k) I always receive True as an output.
Official Specifications M-Track:
bit depth = 24 bit
sample rate = 48 kHz
input via XLR
output via USB
Specifications Computer and Software:
Windows 8.1
I5-3230M # 2,6 GHz
8 GB RAM
Python 3.4.2 with PyAudio 0.2.11 – 32 bit
Audition CS6 Version 5.0.2
Python Code
import pyaudio
import wave
import time
formate = pyaudio.paInt24
channels = 1
framerate = 48000
fileName = 'test ' + '.wav'
chunk = 6144
# output of stream.get_read_available() at different positions
p = pyaudio.PyAudio()
stream = p.open(format=formate,
channels=channels,
rate=framerate,
input=True)
#frames_per_buffer=chunk) # observation c
# COUNTDOWN
for n in range(0, 30):
print(n)
time.sleep(1)
# get data
sampleList = []
for i in range(0, 79):
data = stream.read(chunk, exception_on_overflow = True)
sampleList.append(data)
print('end -', time.strftime('%d.%m.%Y %H:%M:%S', time.gmtime(time.time())))
stream.stop_stream()
stream.close()
p.terminate()
# produce file
file = wave.open(fileName, 'w')
file.setnchannels(channels)
file.setframerate(framerate)
file.setsampwidth(p.get_sample_size(formate))
file.writeframes(b''.join(sampleList))
file.close()
Figure 1: first comparison Audition – PyAudio
image 1
Figure 2: second comparison Audition - Pyaudio
image 2
I am interested in reading gyroscope data by RaspberryPi and Python but I am confused about how to set sample rate of the MPU6050 (accelerometer, gyroscope;datasheet MPU6050) according to I2C clock frequency in order to avoid wrong reading data (for example reading while there is not data in the buffer or reading faster that writing, and so on), in the knowledge that each measure is a 16 bit word.
Is there a relationship between the two clocks?
I did a project with that same chip about 18 months ago. I haven't touched the PI since then, so I don't know how things may have changed in the meantime. In any event, I used the smbus to read the chip. A few things I found out the hard way, reading individual registers was very slow compared to the i2c block read. Also, you had to turn off sleep mode. Sorry I don't have any info on the clock timing, but if you are just trying to get a good read loop, this might help. You don't have to use numpy, but if you plan to manipulate your samples, it's quite helpful. GL/HF.
import smbus
import numpy
# initialize
bus = smbus.SMBus(1)
# turn off sleep mode
bus.write_byte_data(0x68,0x6B,0x00)
# reading in data (this can be in a loop or function call)
sample = numpy.empty(7)
r = bus.read_i2c_block_data(0x68, 0x3B, 14)
sample[0] = (r[0]<<8)+r[1]
sample[1] = (r[2]<<8)+r[3]
sample[2] = (r[4]<<8)+r[5]
sample[3] = (r[6]<<8)+r[7]
sample[4] = (r[8]<<8)+r[9]
sample[5] = (r[10]<<8)+r[11]
sample[6] = (r[12]<<8)+r[13]