mirror of
https://github.com/vale981/fibre_walk_project_code
synced 2025-03-04 09:21:38 -05:00
STUCK: look at first ringdown data
This commit is contained in:
parent
645a92d9e6
commit
388fcccfb9
7 changed files with 314 additions and 12 deletions
20
poetry.lock
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poetry.lock
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@ -701,6 +701,24 @@ files = [
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{file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
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]
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[[package]]
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name = "networkx"
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version = "3.3"
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description = "Python package for creating and manipulating graphs and networks"
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optional = false
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python-versions = ">=3.10"
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files = [
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{file = "networkx-3.3-py3-none-any.whl", hash = "sha256:28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2"},
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{file = "networkx-3.3.tar.gz", hash = "sha256:0c127d8b2f4865f59ae9cb8aafcd60b5c70f3241ebd66f7defad7c4ab90126c9"},
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]
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[package.extras]
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default = ["matplotlib (>=3.6)", "numpy (>=1.23)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
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developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
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doc = ["myst-nb (>=1.0)", "numpydoc (>=1.7)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
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extra = ["lxml (>=4.6)", "pydot (>=2.0)", "pygraphviz (>=1.12)", "sympy (>=1.10)"]
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test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
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[[package]]
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name = "numpy"
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version = "1.26.4"
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@ -1284,4 +1302,4 @@ files = [
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[metadata]
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lock-version = "2.0"
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python-versions = "^3.11"
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content-hash = "145e0c6ca0ebf2f1f6493a208a0a08871fe361b4458d9a7d4d32d83930aa0f4d"
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content-hash = "7cbb6668719eef0958556b0c35997d0c18a2611c0634994da0a3ad2bc00e8fd6"
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@ -10,6 +10,7 @@ python = "^3.11"
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scipy = "^1.13.0"
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click = "^8.1.7"
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matplotlib = "^3.8.4"
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networkx = "^3.3"
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[tool.poetry.group.dev.dependencies]
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151
scripts/experiments/*python:main*
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151
scripts/experiments/*python:main*
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@ -0,0 +1,151 @@
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Python 3.11.9 (main, Apr 2 2024, 08:25:04) [GCC 13.2.0]
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Type 'copyright', 'credits' or 'license' for more information
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IPython 8.24.0 -- An enhanced Interactive Python. Type '?' for help.
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Python 3.11.9 (main, Apr 2 2024, 08:25:04) [GCC 13.2.0]
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Type 'copyright', 'credits' or 'license' for more information
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IPython 8.24.0 -- An enhanced Interactive Python. Type '?' for help.
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In [1]:
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In [2]: %run -i /home/hiro/Documents/org/roam/code/fitting_ringdown/scripts/experiments/003_experimental_ringdown_fft.py # load script buffer
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In [3]:
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In [3]: %run -i /tmp/python-vterm1EdyiK.py # Fourier
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In [4]: %run -i /tmp/python-vtermHclYEt.py # Fourier
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In [5]: %run -i /tmp/python-vtermUXKyDJ.py # CSV
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In [6]: %run -i /tmp/python-vtermIjaHIZ.py # Fourier
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In [7]: %run -i /tmp/python-vtermHYpcEb.py # Fourier
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In [8]: ax3 = ax.twinx()
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In [9]: ax3.plot(freq, np.gradient(np.angle(fft)), linewidth=0.1, color="red", zorder=-10)
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Out[9]: [<matplotlib.lines.Line2D at 0x7f83e3008dd0>]
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In [10]: %run -i /tmp/python-vtermIhW5K3.py # peaks
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In [11]: %run -i /tmp/python-vterm3zwD7E.py # peaks
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In [12]: %run -i /tmp/python-vtermJ1uevg.py # peaks
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---------------------------------------------------------------------------
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ValueError Traceback (most recent call last)
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File /tmp/python-vtermJ1uevg.py:4
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1 # %% peaks
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2 freq_step = freq[1] - freq[0]
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----> 4 peaks, peak_info = scipy.signal.find_peaks(np.abs(fft) ** 2, distance=1e4 / freq_step)
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5 ax.plot(freq[peaks], np.abs(fft[peaks]) ** 2, "x")
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File /nix/store/dbxrivq1l53hnfjsp491yrjnzy651sqg-python3.11-scipy-1.13.0/lib/python3.11/site-packages/scipy/signal/_peak_finding.py:941, in find_peaks(x, height, threshol
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d, distance, prominence, width, wlen, rel_height, plateau_size)
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939 x = _arg_x_as_expected(x)
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940 if distance is not None and distance < 1:
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--> 941 raise ValueError('`distance` must be greater or equal to 1')
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943 peaks, left_edges, right_edges = _local_maxima_1d(x)
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944 properties = {}
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ValueError: `distance` must be greater or equal to 1
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In [13]: %run -i /tmp/python-vtermZ1ZHBs.py # peaks
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---------------------------------------------------------------------------
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ValueError Traceback (most recent call last)
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File /tmp/python-vtermZ1ZHBs.py:4
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1 # %% peaks
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2 freq_step = freq[1] - freq[0]
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----> 4 peaks, peak_info = scipy.signal.find_peaks(np.abs(fft) ** 2, distance=1e4 / freq_step)
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5 ax.plot(freq[peaks], np.abs(fft[peaks]) ** 2, "x")
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File /nix/store/dbxrivq1l53hnfjsp491yrjnzy651sqg-python3.11-scipy-1.13.0/lib/python3.11/site-packages/scipy/signal/_peak_finding.py:941, in find_peaks(x, height, threshol
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d, distance, prominence, width, wlen, rel_height, plateau_size)
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939 x = _arg_x_as_expected(x)
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940 if distance is not None and distance < 1:
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--> 941 raise ValueError('`distance` must be greater or equal to 1')
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943 peaks, left_edges, right_edges = _local_maxima_1d(x)
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944 properties = {}
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ValueError: `distance` must be greater or equal to 1
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In [14]: %run -i /tmp/python-vterm220t3Y.py # peaks
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In [15]: %run -i /tmp/python-vtermoezkoM.py # Fourier
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In [16]: %run -i /tmp/python-vterm3uhE2k.py # plot
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@ -5,22 +5,147 @@ from ringfit.plotting import *
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from ringfit.fit import *
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from rabifun.analysis import *
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import numpy as np
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import scipy
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from collections import OrderedDict
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import networkx as nx
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from functools import reduce
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# %% load data
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path = "../../data/08_05_24/characterization_first"
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path = "../../data/22_05_24/ringdown_with_hybridized_modes"
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scan = ScanData.from_dir(path)
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# %% Fourier
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# window = (0.027751370026589985, 0.027751370026589985 + 0.00001 / 2)
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window = tuple(
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np.array([0.03075207891902308, 0.03075207891902308 + 0.00001]) - 1e-3 - 0.82e-6
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)
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freq, fft = fourier_transform(
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scan.time, scan.output, low_cutoff=1000, high_cutoff=10**8
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scan.time, scan.output, window=window, low_cutoff=20e5, high_cutoff=100e6
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)
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# %% plot
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gc.collect()
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fig = plt.figure("interactive")
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fig.clf()
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ax, ax2 = fig.subplots(1, 2)
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ax.set_yscale("log")
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ax.plot(freq * 10 ** (-6), np.abs(fft) ** 2)
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plot_scan(scan, ax=ax2, linewidth=0.1)
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ax2.set_xlim(0.02, 0.020001)
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# ax.set_yscale("log")
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ax.plot(freq, np.abs(fft))
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ax3 = ax.twinx()
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ax.plot(freq, np.abs(fft.real), linewidth=1, color="red")
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# ax.plot(freq, fft.imag, linewidth=1, color="green")
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# ax3.plot(
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# freq,
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# np.gradient(np.unwrap(np.angle(fft) + np.pi, period=2 * np.pi)),
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# linestyle="--",
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# )
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ax2.set_xlim(*window)
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# plot_scan(scan, ax=ax2, linewidth=0.5, smoothe_output=1e-8)
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freq_step = freq[1] - freq[0]
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peaks, peak_info = scipy.signal.find_peaks(
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np.abs(fft) ** 2, distance=2e6 / freq_step, prominence=3000
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)
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anglegrad = np.gradient(np.unwrap(np.angle(fft) + np.pi, period=2 * np.pi))
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neg_peaks = peaks[anglegrad[peaks] < 0]
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pos_peaks = peaks[anglegrad[peaks] > 0]
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ax.plot(freq[neg_peaks], np.abs(fft[neg_peaks]), "x")
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ax.plot(freq[pos_peaks], np.abs(fft[pos_peaks]), "o")
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# phase_detuning = np.angle(fft[peaks])
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def extract_peak(index, width, sign=1):
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return sign * freq[index - width : index + width], np.abs(
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fft[index - width : index + width]
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)
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# for peak in neg_peaks:
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# ax2.plot(*extract_peak(peak, 10, -1))
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# for peak in pos_peaks:
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# ax2.plot(*extract_peak(peak, 10, 1))
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Ω_guess = 13e6
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δ_guess = 2.69e6
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N = np.arange(1, 3)
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possibilieties = np.concatenate([[2 * δ_guess], N * Ω_guess, N * Ω_guess - δ_guess])
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abs(
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np.array(
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[Ω_guess, 2 * δ_guess, Ω_guess - δ_guess, 2 * Ω_guess, 2 * Ω_guess - δ_guess]
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)
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)
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mode_freqs = freq[peaks]
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final_freqs = [mode_freqs[0]]
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all_diffs = np.abs(
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np.abs(mode_freqs[:, None] - mode_freqs[None, :])[:, :, None]
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- possibilieties[None, None, :]
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)
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all_diffs[all_diffs == 0] = np.inf
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all_diffs[all_diffs > δ_guess / 2] = np.inf
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matches = np.asarray(all_diffs < np.inf).nonzero()
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pairs = np.array(list(zip(*matches, all_diffs[matches])), dtype=int)
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relationships = nx.DiGraph()
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for node, peak in enumerate(peaks):
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relationships.add_node(node, freq=freq[peak])
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for left, right, relationship, diff in pairs:
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if freq[left] > freq[right]:
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left, right = right, left
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if (
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not relationships.has_edge(left, right)
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or relationships[left][right]["weight"] > diff * 1e-6
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):
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relationships.add_edge(
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left, right, weight=diff * 1e-6, type=relationship, freqdis=right - left
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)
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pos = {}
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for node, peak in enumerate(peaks):
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pos[node] = [freq[peak], abs(fft[peak])]
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# nx.draw(relationships, pos, with_labels=True)
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# nx.draw_networkx_edge_labels(relationships, pos)
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# %%
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cycle = nx.find_cycle(relationships, orientation="ignore")
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total = 0
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for s, t, direction in cycle:
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difference = possibilieties[relationships[s][t]["type"]]
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if direction == "reverse":
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difference *= -1
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total += difference
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# %%
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relationships.remove_nodes_from(list(nx.isolates(relationships)))
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spectrum = list(
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sorted(nx.all_simple_paths(relationships, 1, 9), key=lambda x: -len(x))
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)[0]
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for s, t in zip(spectrum[:-1], spectrum[1:]):
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print(s, relationships[s][t])
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for node in spectrum:
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plt.plot(freq[node], abs(fft[node]), "*")
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import numpy as np
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import scipy
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def fourier_transform(
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@ -20,9 +21,9 @@ def fourier_transform(
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if window:
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mask = (window[1] > t) & (t > window[0])
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t = t[mask]
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signal = signal[mask]
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signal = signal[mask] # * scipy.signal.windows.hamming(len(t))
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freq = np.fft.rfftfreq(len(t), t[1] - t[0])
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freq = np.fft.rfftfreq(len(t), t[2] - t[1])
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fft = np.fft.rfft(signal)
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mask = (freq > low_cutoff) & (freq < high_cutoff)
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"""
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directory = Path(directory)
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laser = np.load(directory / "signal_laser.npy")
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laserpath = directory / "signal_laser.npy"
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output = np.load(directory / "signal_outp.npy")
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time = np.load(directory / "time.npy")
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laser = np.load(laserpath) if laserpath.exists() else np.zeros_like(time)
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return cls(laser, output, time, **kwargs)
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@ -119,7 +121,7 @@ class ScanData:
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"""
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return ScanData(
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self._laser, self._output, self._time, max_frequency=max_frequency
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self._laser, self._output.copy(), self._time, max_frequency=max_frequency
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)
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@functools.cache
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@ -32,12 +32,16 @@ def plot_scan(
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steps: bool | int = False,
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normalize=False,
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smoothe_output: bool | float = False,
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max_frequency: float | bool = 0,
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ax=None,
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**kwargs,
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):
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if not (laser or output):
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raise ValueError("At least one of 'laser' or 'output' must be True.")
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if max_frequency:
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data = data.sparsified(max_frequency)
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if smoothe_output:
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if not isinstance(smoothe_output, float):
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smoothe_output = 10 ** (-7)
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