{ "cells": [ { "cell_type": "markdown", "id": "5b76b0eb-c592-4868-bb9a-08ad22895b2b", "metadata": {}, "source": [ "# Rapid tone-pip sequences\n", "\n", "Here we attempt to recreate the auditory stimuli used in [Bianco et al. (2020)](https://doi.org/10.7554/eLife.56073).\n", "\n", "Read the abstract, and check out [Figure 1](https://elifesciences.org/articles/56073#fig1).\n", "\n", "We will recreate the RAN and RANREG sequences here. \n", "\n", "---" ] }, { "cell_type": "raw", "id": "41f974d0-b1bc-469a-beb7-c5ca077c8e43", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "First we import some things, and we will create a :class:`numpy.random.Generator` object with a `random seed `_ so you will get the same output as we." ] }, { "cell_type": "code", "execution_count": 1, "id": "162b46e0-0bdb-43bb-9d31-8ae15bd62e33", "metadata": {}, "outputs": [], "source": [ "from thebeat.core import Sequence, SoundStimulus, SoundSequence\n", "import numpy as np\n", "\n", "rng = np.random.default_rng(seed=123)" ] }, { "cell_type": "code", "execution_count": 2, "id": "d23d30a5-ac0d-4b52-a2f5-eef5cbe313d3", "metadata": { "nbsphinx": "hidden", "tags": [] }, "outputs": [], "source": [ "# We suppress warnings, but let's hide that to avoid confusion\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "c1f06286-42e4-452b-93bc-63cc910c78a9", "metadata": {}, "source": [ "## Creating the RAN sequence" ] }, { "cell_type": "markdown", "id": "b2780c34-ecfd-4790-80a1-2b6e8671311b", "metadata": {}, "source": [ "These sequences were random sequences with a number of properties:\n", "\n", "* The **sequences** were isochronous, had 140 events (i.e. total duration was 7 seconds), and had an inter-onset interval (IOI) of 50 ms.\n", "* The **sounds** had a duration equal to the IOI of 50 ms, so there was no silence in between the sounds.\n", "* The sounds themselves were tone-pips of different frequencies. The frequencies were randomly sampled from twenty values logarithmically spaced between 222 Hz and 2000 Hz.\n", "* An additional constraint was that no two the same frequencies could occur consecutively, which is why we use the ``while`` loop that keeps on sampling if the newly chosen frequency was the same as the last one in the list of already-sampled frequencies." ] }, { "cell_type": "code", "execution_count": 3, "id": "ea6f733b-c220-4ae4-a901-5699b156863f", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Sample the sounds' frequencies with the constraint that no \n", "# two the same frequencies can occur consecutively.\n", "\n", "freqs = np.geomspace(222, 2000, 20)\n", "\n", "freqs_sample = [rng.choice(freqs)]\n", "\n", "for _ in range(139): # sample the other 139 tone freqs\n", " choice = rng.choice(freqs)\n", " while choice == freqs_sample[-1]: \n", " choice = rng.choice(freqs)\n", " freqs_sample.append(choice) " ] }, { "cell_type": "code", "execution_count": 4, "id": "010eeb8b-ca20-4627-af03-f6a0d94a3c31", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Create the sequence\n", "seq = Sequence.generate_isochronous(n_events=140, ioi=50)\n", "\n", "# Create the sounds\n", "stims = [SoundStimulus.generate(freq=freq, \n", " duration_ms=50, \n", " onramp_ms=5, \n", " offramp_ms=5, \n", " ramp_type='raised-cosine') for freq in freqs_sample]\n", "\n", "# Make the trial\n", "RAN_trial = SoundSequence(stims, seq, name=\"RAN sequence\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "06bb0fd6-d17a-4a3d-b5f8-03b870e50656", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "RAN_trial.plot_sequence(linewidth=10, figsize=(10, 2));\n", "# RAN_trial.write_wav('example_RAN.wav')" ] }, { "cell_type": "code", "execution_count": 6, "id": "dc934c59-dfc0-431e-8fcf-dc227da86d5a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is only so you can listen to the sound here. On your computer, simply execute RAN_trial.play()\n", "from IPython.display import Audio\n", "Audio(RAN_trial.samples, rate=RAN_trial.fs)" ] }, { "cell_type": "markdown", "id": "664fe1d0-d8fd-4914-9fe2-7062db3f7356", "metadata": {}, "source": [ "## Creating the RAN_REG sequence\n", "From a random point in the sequence, we start cycling a shorter sequence.\n", "\n", "* At a random point between 3000 and 4000 ms the sequences suddenly becomes regular\n", "* A cycle of 20 frequencies is then repeated until the end of the sequence\n" ] }, { "cell_type": "markdown", "id": "6d26b6dd-49d4-44fb-a1d5-ccad1e3fb3e9", "metadata": {}, "source": [ "### Creating the regular cycle frequencies" ] }, { "cell_type": "code", "execution_count": 7, "id": "1b134046-a71c-4eb0-a4df-8a89fd1af7a3", "metadata": {}, "outputs": [], "source": [ "# Freqs for the regular cycle\n", "freqs = np.geomspace(222, 2000, 20)\n", "cycle_freqs = [rng.choice(freqs)]\n", "\n", "for _ in range(19):\n", " choice = rng.choice(freqs)\n", " while choice == freqs_sample[-1]:\n", " choice = rng.choice(freqs)\n", " cycle_freqs.append(choice)\n", "\n", "change_event_index = int(rng.choice(np.arange(3000/50, 4000/50)))\n", "random_bit = freqs_sample[:change_event_index]\n", "ran_reg = random_bit + cycle_freqs * 4 # combine random bit and 4 cycles (which will be enough)\n", "ran_reg = ran_reg[:140] # Trim to 140 events" ] }, { "cell_type": "markdown", "id": "0e96698c-b344-45df-b986-833a33a4a88b", "metadata": {}, "source": [ "### Combing the random part with the regular cycle\n", "\n", "It will be easiest to start doing that from a certain event, rather than millisecond, so let's assume we can choose an index where it starts." ] }, { "cell_type": "code", "execution_count": 8, "id": "74c0f383-c135-4f74-994a-e7d7f5500be6", "metadata": {}, "outputs": [], "source": [ "change_event_index = int(rng.choice(np.arange(3000/50, 4000/50)))\n", "random_bit = freqs_sample[:change_event_index]\n", "ran_reg = random_bit + cycle_freqs * 4 # combine random bit and 4 cycles (which will be enough)\n", "ran_reg = ran_reg[:140] # Trim to 140 events" ] }, { "cell_type": "markdown", "id": "e76385a7-b193-4962-9293-0086c4fae965", "metadata": {}, "source": [ "### Combine them into a SoundSequence" ] }, { "cell_type": "code", "execution_count": 9, "id": "c866b93f-7a02-4f7b-9e4f-5cd8799abf04", "metadata": {}, "outputs": [ { "data": { "image/png": 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OO9Prr7+e/vSnP6XKysr0xz/+sdDHcS05bHLoTimlkSNHpiOPPDKVlZWlk08+OU2bNm1zz4t6ZPLkyam0tHSD25AhQ1JK6y6vcOutt6bq6upUXl6ezjrrrDRv3ryibbz77rtp0KBBqW3btqmqqipdcsklaeXKlUV9Zs2alU499dRUXl6ejjjiCJeh28ZsrEZKS0vTY489VuizatWq9Itf/CJ16NAhVVZWph/96Efp7bffLtrOm2++mb73ve+lioqK1LFjx3T99den2traoj6TJ09Oxx9/fCorK0s9evQoGoP679JLL03dunVLZWVlqVOnTumss84qBO6U1Amf7b9Dt3ohpXWX7jr88MNTWVlZOuKII9LAgQOLrr2sTlhv3LhxqXfv3qm8vDz16tUrPfzww0XtjmvJoUFKKW3td9sBAABge7RJ3+kGAAAAvjihGwAAADIRugEAACAToRsAAAAyEboBAAAgE6EbAAAAMhG6AQAAIBOhGwAAADIRugEAACCTHbf2BACgvmvZsuXn9hk6dGj84Q9/iJ133jlGjBixBWb12R588MEYPXp0PPbYY9nHuvPOO2PKlClx7733Zh8LALY1DVJKaWtPAgDqs2nTphXd79evX5xxxhnRu3fvwrIDDjgg3nnnnWjYsGEcfPDBW3iGxT766KM4+uij4/LLL49jjjkm+3jLly+Pbt26xR133BGdOnXKPh4AbEu80w0An6Nt27YbLNt77703WL777rtvmQl9jj//+c9RW1sbPXr02CLjlZSURM+ePeP+++8XugHgv/hONwBsJmeccUace+65hfvDhw+Pdu3axcyZM6Nfv35RUVERffv2jZkzZ8bq1avjyiuvjA4dOsQ3vvGN+N3vfrfB9qZOnRpnnnlmtG3bNg477LAYPHhwLFu27HPn8fjjj0ePHj1ixx3/87/10aNHR8uWLaOmpib69+8flZWVccwxx8SkSZOirq4ubrnllqiuro7q6uq46aaboq6urrDu4sWL4yc/+UlUV1dHmzZtonv37nHdddcVjdmrV6+YMGFCvPPOO19izwHA9kvoBoCMamtrY8iQIXHKKafE8OHDY82aNXH++efHz3/+82jSpEnceuutcdRRR8XQoUPjpZdeKqw3derUOOOMM6Jp06Zxyy23xC9/+cuoqamJ88477zPHW7VqVUydOjWqqqo22j5kyJA48sgj4/bbb48999wzzj///Lj22mtj8eLFMWzYsDjttNPirrvuijFjxhTWufjii+PVV1+Nyy67LO6+++644IILikJ5RES7du1i7dq1MWXKlP9hbwHA9sfHywEgo9ra2rjwwguja9euERFRV1cXAwYMiMrKyrj00ksjIqJTp04xduzYGDt2bCEs33TTTVFeXh633357NGjQICIiSktLo3fv3jFhwoTC9v7brFmzora29lNP/nb66afHaaedFhERX/3qV6NPnz4xY8aMePjhhyMi4ogjjohx48bF2LFjo0+fPhERUVNTE4MGDYpvfvObhe2ccMIJRdstKSmJffbZJ6ZPnx69evX6MrsKALZL3ukGgIwaNmwYnTt3Ltw/6KCDIiKiurq6sGyHHXaIAw44IBYvXhwR606E9tJLL0WvXr1i7dq1sWbNmlizZk0cdNBBsffee0dNTc2njrdkyZKI+PTvlx9++OEbzOW/v4fdokWLeOuttwr3W7duHffcc0+MGjUqFixY8Kljf+UrXymMDwCsI3QDQEZNmjSJxo0bF+43atQoIiKaNm1a1K9Ro0axevXqiFh3NvC1a9fG0KFDo6ysrOi2aNGiokD839Zv45NjftInx13fp6SkZIO5fPzxx4X7t9xyS3Tq1CluvfXW6NmzZ/Tq1SuefvrpDbbduHHjwvgAwDo+Xg4A9UzTpk2jQYMGce6558ZRRx21Qftuu+32qevuuuuuEbEuuDdv3nyzzGfPPfeMoUOHRl1dXcyYMSPuvPPO+OlPfxpjx46N/fffv9BvxYoV8bWvfW2zjAkA2wuhGwDqmZ133jnatm0b8+bNizZt2mzSui1atIiIiDfffDMOOeSQzTqvhg0bRkVFRQwcODDGjRsXCxYsKITuurq6WLRoUZx00kmbdUwA2NYJ3QBQD1188cVx1llnxcCBA+PYY4+NkpKSWLx4cUyaNClOPPHE6Nix40bX23///aN58+bxyiuvfOrJ1jbFihUr4pxzzonjjz8+WrRoEbW1tTFy5MgoKSmJ1q1bF/rNnz8/Pvzww2jfvv3/PCYAbE+EbgCoh6qqqmLUqFExfPjwuPTSS6O2tjb22muv6NSpUxx44IGfuW6vXr1i4sSJn3t5sS9ip512itLS0hg5cmS89dZb0aRJkygvL4/f/va3RSdrmzhxYuy7776b/M48AGzvGqSU0taeBACw+cyePTv69u0bzz77bOy7775bZMyTTjopunXrFueff/4WGQ8AthXOXg4A25lWrVpF9+7d4/77798i473wwguxcOHCOPPMM7fIeACwLRG6AWA7dNFFF8Wee+65RcZauXJlDBs2bINLjwEAPl4OAAAA2XinGwAAADIRugEAACAToRsAAAAyEboBAAAgE6EbAAAAMhG6AQAAIBOhGwAAADIRugEAACCT/wOMc8Iok9ifqwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create the sequence\n", "seq = Sequence.generate_isochronous(n_events=140, ioi=50)\n", "\n", "# Create the sounds\n", "stims = [SoundStimulus.generate(freq=freq, \n", " duration_ms=50, \n", " onramp_ms=5, \n", " offramp_ms=5, \n", " ramp_type='raised-cosine') for freq in ran_reg]\n", "\n", "# Make the trial\n", "RANREG_trial = SoundSequence(stims, seq, name=\"RANREG sequence\")\n", "\n", "# Plot it\n", "RANREG_trial.plot_sequence(linewidth=10, figsize=(10, 2));" ] }, { "cell_type": "code", "execution_count": 10, "id": "7dfb68dc-f8dd-4a1b-a31d-6441481aa356", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is only so you can listen to the sound here. On your computer, simply execute RAN_trial.play()\n", "from IPython.display import Audio\n", "Audio(RANREG_trial.samples, rate=RANREG_trial.fs)" ] }, { "cell_type": "markdown", "id": "0f2a94ba-87c7-4e6e-a6f4-369d221077e4", "metadata": {}, "source": [ "## Bonus: Plotting spectograms using Parselmouth" ] }, { "cell_type": "markdown", "id": "b843ec94-c4cd-44a4-86b7-fdc45344c8d7", "metadata": {}, "source": [ "### RAN sequence" ] }, { "cell_type": "code", "execution_count": 11, "id": "6eff272d-848f-4a09-9d46-e4bba2049cb7", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import parselmouth\n", "import matplotlib.pyplot as plt\n", "\n", "# For making spectogram\n", "def draw_spectrogram(spectrogram, dynamic_range=5):\n", " X, Y = spectrogram.x_grid(), spectrogram.y_grid()\n", " sg_db = 10 * np.log10(spectrogram.values)\n", " fig, ax = plt.subplots(figsize=(14, 4), tight_layout=True)\n", " ax.pcolormesh(X, Y, sg_db, vmin=sg_db.max() - dynamic_range, cmap='afmhot')\n", " ax.axes.set_ylim([spectrogram.ymin, 2150])\n", " ax.axes.set_xlabel(\"Time [s]\")\n", " ax.axes.set_ylabel(\"Frequency [Hz]\")\n", "\n", " return fig, ax\n", "\n", " \n", "parselmouth_sound = parselmouth.Sound(values=RAN_trial.samples, sampling_frequency=RAN_trial.fs)\n", "spectogram = parselmouth_sound.to_spectrogram()\n", "\n", "fig, ax = draw_spectrogram(spectogram)\n", "fig.savefig('spectogram.png', dpi=600)" ] }, { "cell_type": "markdown", "id": "5c9631dd-0428-4c70-b573-1caae56683a6", "metadata": {}, "source": [ "### RANREG sequence" ] }, { "cell_type": "code", "execution_count": 12, "id": "f427cde9-9af6-43b5-b2b3-05569484ff9d", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "parselmouth_sound = parselmouth.Sound(values=RANREG_trial.samples, sampling_frequency=RANREG_trial.fs)\n", "spectogram = parselmouth_sound.to_spectrogram()\n", "\n", "fig, ax = draw_spectrogram(spectogram)\n", "fig.savefig('ranreg.png', dpi=600)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 }