Merge branch 'drone_tests' into develop
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88
.drone.yml
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88
.drone.yml
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kind: pipeline
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type: docker
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name: archlinux
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clone:
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depth: 50
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steps:
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- name: archlinux_build
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image: archlinux_build
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pull: if-not-exists
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volumes:
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- name: archlinux_ccache
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path: /root/.ccache
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commands:
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# The following command is not required, we included this in the docker
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# image of archlinux_build
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# - pacman -S --noconfirm ccache openblas fftw pulseaudio pybind11
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- git submodule update --init --recursive
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- cmake .
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# More than two makes ascee2 irresponsive for now
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- make -j2
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- name: archlinux_test
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image: archlinux_build
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pull: if-not-exists
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commands:
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# The following command is not required, we included this in the docker
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# image of archlinux_build
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# - pacman -S --noconfirm openblas python-pytest fftw pulseaudio python-pip python-scipy python-h5py
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- pip install -r requirements.txt
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- pip install .
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- pytest
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# - name: release-arch
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# commands:
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# -
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volumes:
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- name: archlinux_ccache
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host:
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path: /tmp/archlinux_ccache
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---
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kind: pipeline
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type: docker
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name: ubuntu
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clone:
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depth: 3
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volumes:
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- name: archlinux_ccache
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path: /root/.ccache
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steps:
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- name: ubuntu_build
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image: ubuntu
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pull: if-not-exists
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volumes:
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- name: ubuntu_ccache
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path: /root/.ccache
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commands:
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# The following commands are not required, we included this in the docker
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# image of ubuntu_build
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#- apt update
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#- apt install -y git cmake python3-pybind11 libopenblas-dev python3-pip python3-scipy libusb-1.0-0-dev libpulse-dev python3-h5py fftw-dev
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- git submodule update --init --recursive
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- cmake .
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# More than two makes ascee2 irresponsive for now
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- make -j2
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- name: ubuntu_test
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image: ubuntu
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pull: if-not-exists
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commands:
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# The following commands are not required, we included this in the docker
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# image of ubuntu_build
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#- apt update
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#- apt install -y python3-pytest fftw pulseaudio python3-pip python3-scipy python3-h5py
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- pip install -r requirements.txt
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- pip install .
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- pytest-3
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volumes:
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- name: ubuntu_ccache
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host:
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path: /tmp/ubuntu_ccache
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@ -1,5 +1,7 @@
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# Library for Acoustic Signal Processing
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[![Build Status](https://drone.ascee.nl/api/badges/ASCEE/lasp/status.svg)](https://drone.ascee.nl/ASCEE/lasp)
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Welcome to LASP: Library for Acoustic Signal Processing. LASP is a C++ library
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with a Python interface which is supposed to acquire and process (multi) sensor data in real time on a PC and output results.
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@ -32,7 +34,7 @@ If you have any question(s), please feel free to contact us: info@ascee.nl.
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## Dependencies
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- `$ sudo apt install libopenblas-dev python3-pip python3-scipy libusb-dev libpulse-dev cmake-curses-gui python3-h5py`
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- `$ sudo apt install python3-pybind11 libopenblas-dev python3-pip python3-scipy libusb-1.0-0-dev libpulse-dev cmake-curses-gui python3-h5py`
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- `$ pip3 install --user -r requirements.txt`
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@ -2,15 +2,10 @@
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import numpy as np
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from lasp import SeriesBiquad, AvPowerSpectra
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from lasp.filter import SPLFilterDesigner
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import matplotlib.pyplot as plt
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from scipy.signal import sosfreqz
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# plt.close('all')
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plt.close('all')
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# def test_cppslm2():
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# """
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# Generate a sine wave, now A-weighted
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# """
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fs = 48000
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omg = 2*np.pi*1000
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[project] # Project metadata
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name = "lasp"
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readme = "README.md"
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requires-python = ">=3.8"
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license = { "file" = "LICENSE" }
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authors = [{ "name" = "J.A. de Jong et al.", "email" = "info@ascee.nl" }]
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classifiers = [
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"Topic :: Scientific/Engineering",
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"Programming Language :: Python :: 3.8",
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"Operating System :: POSIX :: Linux",
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"Operating System :: Microsoft :: Windows",
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]
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# urls = { "Documentation" = "https://" }
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dynamic = ["version", "description"]
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@ -1,2 +1,3 @@
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appdirs
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dataclasses_json
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matplotlib
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26
setup.py
26
setup.py
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import glob
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import glob, os
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import platform
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from setuptools import setup
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if 'Linux' in platform.platform():
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extension = list(glob.glob('src/lasp/lasp_cpp.cpython*'))
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if len(extension) == 0:
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ext_name_glob = 'lasp_cpp.cpython*'
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extensions = list(glob.glob('src/lasp/' + ext_name_glob))
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# Split of path from file.
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ext_names = [os.path.split(a)[1] for a in extensions]
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print(extensions)
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if len(extensions) == 0:
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raise RuntimeError('Please first run CMake to build extension')
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elif len(extension) > 1:
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elif len(extensions) > 1:
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raise RuntimeError('Too many extension files found')
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pkgdata = extension
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pkgdata = ext_names
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else:
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raise RuntimeError('Not yet Windows-proof')
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keywords = ["DSP", "DAQ", "Signal processing"]
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with open('README.md', 'r') as f:
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readme = f.read()
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setup(
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name="lasp",
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@ -40,12 +43,11 @@ setup(
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classifiers=classifiers,
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keywords=keywords,
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license="MIT",
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readme=readme,
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dependencies=["numpy", "scipy", "appdirs", "h5py", "appdirs",
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"dataclasses_json"],
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"dataclasses_json"],
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package_dir={"": "src"},
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packages=['lasp', 'lasp.filter', 'lasp.tools'],
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data_files = pkgdata,
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include_package_data=True,
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package_dir={'': 'src'},
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package_data={'lasp': pkgdata},
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python_requires='>=3.8',
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)
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@ -8,8 +8,6 @@ Created on Mon Jan 15 19:45:33 2018
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import numpy as np
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from lasp import AvPowerSpectra, Window
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import matplotlib.pyplot as plt
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# plt.close('all')
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def test_aps1():
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nfft = 16384
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import numpy as np
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from lasp import cppSLM
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from lasp.filter import SPLFilterDesigner
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import matplotlib.pyplot as plt
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def test_cppslm1():
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"""
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Generate a sine wave
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"""
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fs = 48000
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omg = 2*np.pi*1000
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omg = 2 * np.pi * 1000
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slm = cppSLM.fromBiquads(fs, 2e-5, 1, 0.125, [1.,0,0,1,0,0])
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slm = cppSLM.fromBiquads(fs, 2e-5, 1, 0.125,
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np.array([[1., 0, 0, 1, 0, 0]]).T)
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t = np.linspace(0, 10, 10*fs, endpoint=False)
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t = np.linspace(0, 10, 10 * fs, endpoint=False)
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# Input signal with an rms of 1 Pa
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in_ = np.sin(omg*t)*np.sqrt(2)
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in_ = np.sin(omg * t) * np.sqrt(2)
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# Compute overall RMS
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rms = np.sqrt(np.sum(in_**2)/in_.size)
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rms = np.sqrt(np.sum(in_**2) / in_.size)
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# Compute overall level
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level = 20*np.log10(rms/2e-5)
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level = 20 * np.log10(rms / 2e-5)
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# Output of SLM
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out = slm.run(in_)
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# Output of SLM should be close to theoretical
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# level, at least for reasonable time constants
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# (Fast, Slow etc)
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assert(np.isclose(out[-1,0], level))
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assert (np.isclose(out[-1, 0], level))
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def test_cppslm2():
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Generate a sine wave, now A-weighted
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"""
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fs = 48000
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omg = 2*np.pi*1000
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omg = 2 * np.pi * 1000
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filt = SPLFilterDesigner(fs).A_Sos_design()
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slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125, filt.flatten(), [1.,0,0,1,0,0])
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slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125,
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filt.flatten(), # Pre-filter coefs
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np.array([[1., 0, 0, 1, 0, 0]]).T # Bandpass coefs
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)
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t = np.linspace(0, 10, 10*fs, endpoint=False)
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t = np.linspace(0, 10, 10 * fs, endpoint=False)
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# Input signal with an rms of 1 Pa
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in_ = np.sin(omg*t) *np.sqrt(2)
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in_ = np.sin(omg * t) * np.sqrt(2)
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# Compute overall RMS
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rms = np.sqrt(np.sum(in_**2)/in_.size)
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rms = np.sqrt(np.sum(in_**2) / in_.size)
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# Compute overall level
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level = 20*np.log10(rms/2e-5)
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level = 20 * np.log10(rms / 2e-5)
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# Output of SLM
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out = slm.run(in_)
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# Output of SLM should be close to theoretical
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# level, at least for reasonable time constants
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# (Fast, Slow etc)
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assert np.isclose(out[-1,0], level, atol=1e-2)
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assert np.isclose(out[-1, 0], level, atol=1e-2)
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def test_cppslm3():
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fs = 48000
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omg = 2*np.pi*1000
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omg = 2 * np.pi * 1000
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filt = SPLFilterDesigner(fs).A_Sos_design()
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slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125, filt.flatten(), [1.,0,0,1,0,0])
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t = np.linspace(0, 10, 10*fs, endpoint=False)
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slm = cppSLM.fromBiquads(fs, 2e-5, 0, 0.125,
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filt.flatten(),
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np.array([[1., 0, 0, 1, 0, 0]]).T)
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t = np.linspace(0, 10, 10 * fs, endpoint=False)
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in_ = 10*np.sin(omg*t) * np.sqrt(2)+np.random.randn()
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in_ = 10 * np.sin(omg * t) * np.sqrt(2) + np.random.randn()
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# Compute overall RMS
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rms = np.sqrt(np.sum(in_**2)/in_.size)
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rms = np.sqrt(np.sum(in_**2) / in_.size)
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# Compute overall level
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level = 20*np.log10(rms/2e-5)
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level = 20 * np.log10(rms / 2e-5)
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# Output of SLM
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out = slm.run(in_)
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Lpeak = 20*np.log10(np.max(np.abs(in_)/2e-5))
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Lpeak = 20 * np.log10(np.max(np.abs(in_) / 2e-5))
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Lpeak
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slm.Lpeak()
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assert np.isclose(out[-1,0], slm.Leq()[0][0], atol=1e-2)
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assert np.isclose(Lpeak, slm.Lpeak()[0][0], atol=2e0)
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assert np.isclose(out[-1, 0], slm.Leq()[0], atol=1e-2)
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assert np.isclose(Lpeak, slm.Lpeak()[0], atol=2e0)
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if __name__ == '__main__':
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@ -5,8 +5,6 @@ Testing code for power spectra
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"""
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import numpy as np
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from lasp import PowerSpectra, Window
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# import matplotlib.pyplot as plt
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# plt.close('all')
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def test_ps():
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"""
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#!/usr/bin/python3
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import numpy as np
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from lasp import SLM
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nframes = 0
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samplerate = 48000
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omg = 2*np.pi*1000
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# def mycallback(input_, nframes, streamtime):
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# t = np.linspace(streamtime, streamtime + nframes/samplerate,
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# nframes)[np.newaxis,:]
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# outp = 0.1*np.sin(omg*t)
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# return outp, 0
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# if __name__ == '__main__':
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# pa = RtAudio()
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# count = pa.getDeviceCount()
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# # dev = pa.getDeviceInfo(0)
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# for i in range(count):
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# dev = pa.getDeviceInfo(i)
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# print(dev)
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# outputparams = {'deviceid': 0, 'nchannels': 1, 'firstchannel': 0}
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# pa.openStream(outputparams, None , Format_FLOAT64,samplerate, 512, mycallback)
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# pa.startStream()
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# input()
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# pa.stopStream()
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# pa.closeStream()
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|
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