init

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+.coverage.*
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+docs/autosummary/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*.ipynb
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; see https://github.com/pyenv/pyenv/issues/528
+.python-version
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and https://pdm.fming.dev
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+env.d/
+venv.d/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# VSCode settings
+.vscode/
+

README.md

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+mysignal
+=======

mysignal/__init__.py

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+__version__ = '0.1.0'

mysignal/mysignal.py

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+import functools
+import numpy as np
+import pandas as pd
+from scipy.fft import fft, fftfreq
+from scipy.signal import find_peaks
+from scipy.signal import correlate
+import matplotlib.pyplot as plt
+
+
+def normalize_signal(s):
+    """
+    Set the mean at 0, and max at 1.
+    """
+    s -= s.mean()
+    s /= s.max()
+    return s
+
+
+def get_global_mode(modes):
+    """
+    For a series of modes, compute the period of the resulting signal.
+
+    Ex (1, 2, 3) -> 6
+    """
+    return functools.reduce(np.lcm, modes)
+
+
+def sync_phase_signal_in(df_, modes, baseperiod):
+    df = df_.copy()
+    dt = np.mean(np.diff(df['time']))
+    N = len(df['time'])
+    t = np.linspace(0., N*dt, N, endpoint=False)
+    global_mode = get_global_mode(modes)
+
+    signal_ref = 0
+    for mode in modes:
+        signal_ref += np.sin(2 * np.pi * t / (mode * baseperiod))
+    correlation = correlate(df['RHset'], signal_ref, mode='full', method='direct')
+    corr_peaks = find_peaks(correlation, distance=.95 * baseperiod * global_mode)[0]
+    x_corr = np.arange(0, len(correlation), 1) - (N-4)
+    # Take the first positive peak
+    corr_idx = corr_peaks[corr_peaks>np.argwhere(x_corr>0)[0][0]][0]
+    idx = x_corr[corr_idx]
+
+    df = df[idx:]
+    df['time'] -= df['time'].iloc[0]
+    df = df.reset_index()
+    del df['index']
+    return df
+
+
+def resample_df(df_, modes, baseperiod, id_t='time', id_in='RH', id_out='dm_m'):
+
+    t_origin = df_[id_t].to_numpy()
+    in_origin = df_[id_in].to_numpy()
+    out_origin = df_[id_out].to_numpy()
+
+
+    dt = np.mean(np.diff(t_origin))
+
+
+    signal_period = get_global_mode(modes) * baseperiod
+    alpha = np.floor(len(t_origin) * dt / signal_period)
+    print(alpha)
+
+    N = int(np.round(alpha * signal_period / dt))
+    print(f'N_max: {len(t_origin)}, N: {N}')
+    # reconstructed time (strictly evenly spaced)
+    t = np.linspace(0., N*dt, N, endpoint=False)
+    signal_in = in_origin[:N]
+    signal_out = out_origin[:N]
+    return t, dt, signal_in, signal_out
+
+
+def find_prominent_frequencies(signal, sampling_rate, num_frequencies=1):
+    # Compute the FFT of the signal
+    fft_result = fft(signal)
+
+    # Compute the power spectrum
+    power_spectrum = np.abs(fft_result) ** 2
+
+    # Generate the frequency axis
+    freqs = np.fft.fftfreq(len(signal), d=1/sampling_rate)
+
+    # Only consider the positive frequencies
+    positive_freqs = freqs[:len(freqs) // 2]
+    positive_power_spectrum = power_spectrum[:len(power_spectrum) // 2]
+
+    # Find the indices of the peaks in the power spectrum
+    peaks, _ = find_peaks(positive_power_spectrum)
+
+    # Get the indices of the highest peaks
+    highest_peaks = np.argsort(positive_power_spectrum[peaks])[-num_frequencies:]
+
+    # Get the corresponding frequencies of the highest peaks
+    prominent_frequencies = positive_freqs[peaks][highest_peaks]
+
+    return prominent_frequencies
+
+
+def get_IQ_coeff(signal, sampling_rate, frequencies, debug=False):
+    N = len(signal)
+
+    # if not hasattr(frequencies, '__iter__'):
+    #     frequencies = (frequencies,)
+
+    # FFT
+    Yf = fft(signal)[:N//2]
+    xf = fftfreq(N, 1/sampling_rate)[:N//2]
+
+    # Get coefficients
+    Bcos_fft = []
+    Bsin_fft = []
+
+    if debug:
+        fig, ax = plt.subplots(nrows=2, figsize=(8, 6))
+
+    for f in frequencies:
+        idx = np.argmin(np.abs(xf - f))
+        print(f'distance xf f: {np.abs(xf[idx] - f)/f}')
+        #print('position freq:')
+        #print(idx, len(xf), idx / len(xf))
+        Y_f = Yf[idx]
+        Bcos_fft.append(2.0 * Y_f.real / N)
+        Bsin_fft.append(-2.0 * Y_f.imag / N)
+
+        if debug:
+            ax[0].plot(xf[idx-10:idx+10], Yf[idx-10:idx+10].real, '+-');
+            ax[0].plot(xf[idx], Yf[idx].real, 'o', label=f'f={f:.1e}');
+            ax[1].plot(xf[idx-10:idx+10], Yf[idx-10:idx+10].imag, 'x-');
+            ax[1].plot(xf[idx], Yf[idx].imag, 'o', label=f'f={f:.1e}');
+
+    if debug:
+        plt.tight_layout()
+        ax[0].set_title('cos')
+        ax[0].legend()
+        ax[1].set_title('sin')
+        ax[1].legend()
+
+    # Display
+    for i, f in enumerate(frequencies):
+        print(f"Freq {f:.2e} Hz, Period {1/f:.1f} s: A_I = {Bsin_fft[i]:.2e}, A_Q = {Bcos_fft[i]:.2e}, tan delta = {Bcos_fft[i]/Bsin_fft[i]:.2f}")
+    return np.array(Bcos_fft), np.array(Bsin_fft)

requirements.txt

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+numpy
+scipy
+pandas

setup.py

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+
+import os
+import re
+from setuptools import setup, find_packages
+
+def read_version(name):
+    with open(os.path.join(os.path.dirname(__file__), name, '__init__.py')) as f:
+        for line in f:
+            match = re.match(r"^__version__ = ['\"]([^'\"]*)['\"]", line)
+            if match:
+                return match.group(1)
+        raise RuntimeError("Unable to find version string.")
+
+def parse_requirements(filename):
+    with open(filename, 'r') as file:
+        return file.read().splitlines()
+
+# Get the long description from the README file
+here = os.path.abspath(os.path.dirname(__file__))
+with open(os.path.join(here, 'README.md'), encoding='utf-8') as f:
+	long_description = f.read()
+
+
+setup(
+    name='mysignal',  # Required
+    version      = read_version('mysignal'),
+    description='A short description of the project',  # Optional
+    long_description=long_description,  # Optional
+    long_description_content_type='text/markdown',  # Optional (see note above)
+    url='https://github.com/yourusername/yourproject',  # Optional
+    author='Your Name',  # Optional
+    author_email='your.email@example.com',  # Optional
+
+    # Automatically find packages in the current directory
+    packages=find_packages(),  # Required
+
+    # Include additional files specified in MANIFEST.in
+    include_package_data=True,  # Optional
+
+    # Define your dependencies in a separate file
+    install_requires=parse_requirements('requirements.txt'),  # Optional
+
+    # Classifiers help users find your project by categorizing it
+    classifiers=[
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Developers',
+        'Topic :: Software Development :: Build Tools',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
+    ],
+
+    # Specify the Python versions you support
+    python_requires='>=3.6, <4',
+
+    # Specify additional groups of dependencies (e.g., for testing)
+   # extras_require={
+   #     'dev': ['check-manifest'],
+   #     'test': ['coverage'],
+   # },
+
+    # You can also specify entry points for command-line scripts
+   # entry_points={
+   #     'console_scripts': [
+   #         'sample=sample:main',
+   #     ],
+   # },
+)
+