{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SciPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is SciPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SciPy stands for (Sci)entific (Py)thon. It is an open-source Python library that contains modules for scientific computing such as linear algrbra, optimization, image/signal processing,, statistics, and many more. SciPy uses the NumPy `array` as it's base data struture, but also builds upon `Pandas`, `Matplotlib`, and `SymPy`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd \n", "from allensdk.core.cell_types_cache import CellTypesCache\n", "from allensdk.api.queries.cell_types_api import CellTypesApi\n", "import matplotlib.pyplot as plt\n", "from scipy import stats \n", "from scipy import linalg\n", "ctc = CellTypesCache(manifest_file='cell_types/manifest.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will be demonstrating SciPy using the Allen Cell Types Dataset. We will discuss what this dataset contains and how to navigate through it in a later chapter. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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reporter_statuscell_soma_locationspeciesnamestructure_layer_namestructure_area_idstructure_area_abbrevtransgenic_linedendrite_typeapical...trough_t_ramptrough_t_short_squaretrough_v_long_squaretrough_v_ramptrough_v_short_squareupstroke_downstroke_ratio_long_squareupstroke_downstroke_ratio_rampupstroke_downstroke_ratio_short_squarevm_for_sagvrest
id
525011903None[273.0, 354.0, 216.0]Homo SapiensH16.03.003.01.14.02312113FroLspinyintact...4.1349871.375253-53.968754-59.510420-71.1979192.8954612.5598763.099787-88.843758-70.561035
528642047None[69.0, 254.0, 96.0]Homo SapiensH16.06.009.01.02.06.05512141MTGaspinyNA...NaN1.051160-67.468758NaN-70.8750021.891881NaN1.989616-101.000000-69.209610
537256313None[322.0, 255.0, 92.0]Homo SapiensH16.03.006.01.05.02412141MTGspinytruncated...5.6945471.389900-52.125004-51.520836-72.9000023.1211823.4645283.054681-87.531250-72.628105
519832676None[79.0, 273.0, 91.0]Homo SapiensH16.03.001.01.09.01312141MTGspinytruncated...9.9627801.211020-53.875004-52.416668-73.6937534.5748653.8179884.980603-84.218758-72.547661
596020931None[66.0, 220.0, 105.0]Homo SapiensH17.06.009.11.04.02412141MTGaspinyNA...14.6673401.336668-63.593754-63.239583-75.5187531.4528901.4417541.556087-82.531250-74.260269
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5 rows × 70 columns

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" ], "text/plain": [ " reporter_status cell_soma_location species \\\n", "id \n", "525011903 None [273.0, 354.0, 216.0] Homo Sapiens \n", "528642047 None [69.0, 254.0, 96.0] Homo Sapiens \n", "537256313 None [322.0, 255.0, 92.0] Homo Sapiens \n", "519832676 None [79.0, 273.0, 91.0] Homo Sapiens \n", "596020931 None [66.0, 220.0, 105.0] Homo Sapiens \n", "\n", " name structure_layer_name structure_area_id \\\n", "id \n", "525011903 H16.03.003.01.14.02 3 12113 \n", "528642047 H16.06.009.01.02.06.05 5 12141 \n", "537256313 H16.03.006.01.05.02 4 12141 \n", "519832676 H16.03.001.01.09.01 3 12141 \n", "596020931 H17.06.009.11.04.02 4 12141 \n", "\n", " structure_area_abbrev transgenic_line dendrite_type apical ... \\\n", "id ... \n", "525011903 FroL spiny intact ... \n", "528642047 MTG aspiny NA ... \n", "537256313 MTG spiny truncated ... \n", "519832676 MTG spiny truncated ... \n", "596020931 MTG aspiny NA ... \n", "\n", " trough_t_ramp trough_t_short_square trough_v_long_square \\\n", "id \n", "525011903 4.134987 1.375253 -53.968754 \n", "528642047 NaN 1.051160 -67.468758 \n", "537256313 5.694547 1.389900 -52.125004 \n", "519832676 9.962780 1.211020 -53.875004 \n", "596020931 14.667340 1.336668 -63.593754 \n", "\n", " trough_v_ramp trough_v_short_square \\\n", "id \n", "525011903 -59.510420 -71.197919 \n", "528642047 NaN -70.875002 \n", "537256313 -51.520836 -72.900002 \n", "519832676 -52.416668 -73.693753 \n", "596020931 -63.239583 -75.518753 \n", "\n", " upstroke_downstroke_ratio_long_square \\\n", "id \n", "525011903 2.895461 \n", "528642047 1.891881 \n", "537256313 3.121182 \n", "519832676 4.574865 \n", "596020931 1.452890 \n", "\n", " upstroke_downstroke_ratio_ramp \\\n", "id \n", "525011903 2.559876 \n", "528642047 NaN \n", "537256313 3.464528 \n", "519832676 3.817988 \n", "596020931 1.441754 \n", "\n", " upstroke_downstroke_ratio_short_square vm_for_sag vrest \n", "id \n", "525011903 3.099787 -88.843758 -70.561035 \n", "528642047 1.989616 -101.000000 -69.209610 \n", "537256313 3.054681 -87.531250 -72.628105 \n", "519832676 4.980603 -84.218758 -72.547661 \n", "596020931 1.556087 -82.531250 -74.260269 \n", "\n", "[5 rows x 70 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Download meatadata for only human cells \n", "human_cells = pd.DataFrame(ctc.get_cells(species=[CellTypesApi.HUMAN])).set_index('id')\n", "\n", "# Download electrophysiology data \n", "ephys_df = pd.DataFrame(ctc.get_ephys_features()).set_index('specimen_id')\n", "\n", "# Combine our ephys data with our metadata for human cells \n", "human_ephys_df = human_cells.join(ephys_df)\n", "human_ephys_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SciPy for two-sample statistics" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Set up our two samples \n", "spiny_df = human_ephys_df[human_ephys_df['dendrite_type']== 'spiny']\n", "aspiny_df = human_ephys_df[human_ephys_df['dendrite_type']== 'aspiny']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most often, the goal of our hypothesis testing is to test whether or not two distributions are different, or if a distribution has a different mean than the underlying population distribution.\n", "\n", "If we know our distributions are normal (i.e. they're generated from a normal distribution!) we could use parametric statistics to test our hypothesis. To test for differences between normal populations, we can use the independent t-test in our stats package: `stats.ttest_ind()`. If we had paired samples, we would use a dependent t-test: `stats.ttest_rel()`.\n", "\n", "\n", "If one of our populations is skewed, however, we cannot use a t-test. A t-test assumes that the populations are normally distributed. For skewed populations, we can use either the Mann-Whitney U (for independent samples: `stats.mannwhitneyu()`) or the Wilcoxon Signed Rank Test (for dependent/paired samples: `stats.wilcoxon()`)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we will run a statistical test that compares `aspiny_df['tau']` to `spiny_df['tau']`. Before we decide what test to run, we must first check the skewness of our data. To test for skewness, we can use `stats.skewtest()`. **If the skew test gives us a p-value of less than 0.05, the population is skewed.**" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spiny skew test pvalue: 1.4148485388194806e-12\n", "Aspiny skew test pvalue: 1.1827442418538926e-09\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Subselect our samples \n", "sample_1 = spiny_df['tau']\n", "sample_2 = aspiny_df['tau']\n", "\n", "# Run the skew test\n", "stats_1, pvalue_1 = stats.skewtest(sample_1) \n", "stats_2, pvalue_2 = stats.skewtest(sample_2)\n", "\n", "# Print the p-value of both skew tests\n", "print('Spiny skew test pvalue: ' + str(pvalue_1))\n", "print('Aspiny skew test pvalue: ' + str(pvalue_2))\n", "\n", "# Plot our distributions \n", "plt.hist(sample_1)\n", "plt.show()\n", "plt.hist(sample_2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our pvalues indicate that both of our samples are skewed, therefore we will continure with the Mann-Whitney U test. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MannwhitneyuResult(statistic=4759.0, pvalue=3.864465154121331e-18)\n" ] } ], "source": [ "print(stats.mannwhitneyu(sample_1, sample_2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SciPy for Linear Algebra" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SciPy can be used to find solutions to mathematical algorithms as well. The package has a module with many tools that are helpful with linear algebra. The funtions availabe can help with solving the determinant of a matrix, solve for eigen values, and other liniar algebra problems. After importing the linear algebra module, `linalg` from the SciPy package, it is important to use the syntax `linalg.function()` when executing a function from the module. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `linalg.det()` method is used to solve for the the determinant of a matrix. This function only takes in square matrices as an argument, inputting a non-square matrix will result in an error. Please visit here for the original documentation. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 3 5 2]\n", " [6 3 9 7]\n", " [2 7 8 5]\n", " [9 4 1 8]]\n" ] } ], "source": [ "# Assign our square matrix\n", "matrix = np.array([[1,3,5,2], [6,3,9,7], [2,7,8,5], [9,4,1,8]])\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-237.00000000000006\n" ] } ], "source": [ "print(linalg.det(matrix))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `linalg.lu()` method is used to solve the LU decomposition of a matrix. It will return the permutation matrix, p, the lower triangular matrix with unit diagonal elements, l, and the upper triangular, u. You can look at the SciPy documentation for more information. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Permutation matrix:\n", "[[0. 0. 0. 1.]\n", " [0. 0. 1. 0.]\n", " [0. 1. 0. 0.]\n", " [1. 0. 0. 0.]]\n", "\n", "\n", "Lower triangular:\n", "[[1. 0. 0. 0. ]\n", " [0.22222222 1. 0. 0. ]\n", " [0.66666667 0.05454545 1. 0. ]\n", " [0.11111111 0.41818182 0.20689655 1. ]]\n", "\n", "\n", "Upper Triangle\n", "[[ 9. 4. 1. 8. ]\n", " [ 0. 6.11111111 7.77777778 3.22222222]\n", " [ 0. 0. 7.90909091 1.49090909]\n", " [ 0. 0. 0. -0.54482759]]\n" ] } ], "source": [ "# Assign our matrix \n", "p, l, u = linalg.lu(matrix)\n", "print('Permutation matrix:')\n", "print(p)\n", "print('\\n')\n", "print('Lower triangular:')\n", "print(l)\n", "print('\\n')\n", "print('Upper Triangle')\n", "print(u)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have p, l, and u, we can used a funtion from numpy, `np.dot()` to execute the dot product of l and u to return our matrix. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[9. 4. 1. 8.]\n", " [2. 7. 8. 5.]\n", " [6. 3. 9. 7.]\n", " [1. 3. 5. 2.]]\n" ] } ], "source": [ "print(np.dot(l,u))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also find the eigen values and eigen vectors with SciPy. The method `linalg.eig()` takes in a complex or real matrix and returns its eigenvalues. Please visit the SciPy documentation for more help." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[20.15126564+0.j 1.64808546+0.83387563j 1.64808546-0.83387563j\n", " -3.44743656+0.j ]\n", "\n", "\n", "[[-2.94498514e-01+0.j -4.63909706e-01+0.08050438j\n", " -4.63909706e-01-0.08050438j -2.00900213e-04+0.j ]\n", " [-5.98355615e-01+0.j -1.69258696e-01+0.00537595j\n", " -1.69258696e-01-0.00537595j -8.69603794e-01+0.j ]\n", " [-5.83737468e-01+0.j -2.96214773e-01-0.07015942j\n", " -2.96214773e-01+0.07015942j 4.14828984e-01+0.j ]\n", " [-4.63132542e-01+0.j 8.10533088e-01+0.j\n", " 8.10533088e-01-0.j 2.67779977e-01+0.j ]]\n" ] } ], "source": [ "eigen_values, eigen_vectors = linalg.eig(matrix) \n", "print(eigen_values)\n", "print('\\n')\n", "print(eigen_vectors) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, SciPy can be used to solve linear systems of equations. Please visit the SciPy documentation for more help." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.04219409]\n", " [ 0.76793249]\n", " [-0.31223629]\n", " [ 0.60759494]]\n" ] } ], "source": [ "array = [[2],[4],[6],[8]]\n", "print(linalg.solve(matrix, array))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SciPy for FFT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first generate a sine wave. We'll then generate a second sine wave and add these together to understand what a fourier transform of this data would look like. Sine waves are defined by their frequency, ampltitude, and and phase. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You've created a complex signal with two sin waves, it looks like this:\n", "[ 0. 1.47439991 2.08519495 1.48970011 0.07282654 -1.28516247\n", " -1.73971525 -0.9915122 0.53292413 1.93848187 2.40138863 1.66610567\n", " 0.19943724 -1.09111077 -1.40482347 -0.53084714 1.02457393 2.34344933\n", " 2.64978051 1.77764376 0.27124849 -0.94338912 -1.11709493 -0.12950467\n", " 1.43829796 2.65429147 2.79773083 1.79391154 0.25921309 -0.87075038\n", " -0.90555508 0.18351981 1.74615215 2.84526973 2.82232648 1.69456043\n", " 0.14458863 -0.89138407 -0.78833049 0.39055267 1.93196998 2.90282491\n", " 2.71311393 1.47185272 -0.07867731 -1.01069639 -0.77059328 0.48712609\n", " 1.99306173 2.82707791 2.47339433 1.13175262 -0.40305218 -1.22060289\n", " -0.84403858 0.48231033 1.94034552 2.63174967 2.11992873 0.69342222\n", " -0.80812029 -1.5004171 -0.98795545 0.39745719 1.79689699 2.34251012\n", " 1.68108704 0.18717967 -1.26280101 -1.81923201 -1.17176498 0.26350358\n", " 1.59508858 1.99394982 1.19365596 -0.34884385 -1.72882574 -2.13951596\n", " -1.35872776 0.11715217 1.37265455 1.62552844 0.69867805 -0.87295939\n", " -2.16506855 -2.4215031 -1.51038607 -0.0036222 1.16814322 1.27697384\n", " 0.23680688 -1.34459205 -2.53222567 -2.62786482 -1.59121986 -0.06421317\n", " 1.01628945 0.98366972 -0.15628002 -1.72882537 -2.79729789 -2.72811589\n", " -1.57297081 -0.03762605 0.94384942 0.77257042 -0.4538759 -2.00028872\n", " -2.93735296 -2.7022398 -1.43812718 0.0921744 0.96638583 0.6591204\n", " -0.64118171 -2.1459351 -2.94212909 -2.54310883 -1.18216036 0.32820494\n", " 1.08638016 0.64553743 -0.71684373 -2.16638653 -2.81517573 -2.25741512\n", " -0.81424972 0.65993593 1.29289322 0.7206598 -0.692914 -2.07569135\n", " -2.5733991 -1.86500303 -0.35640926 1.06435804 1.56281436 0.86137393\n", " -0.59324003 -1.89952345 -2.24506578 -1.39667963 0.15888389 1.50852037\n", " 1.86355342 1.03545473 -0.45047402 -1.67203558 -1.86649794 -0.89075814\n", " 0.69228366 1.95324482 2.15686085 1.20548496 -0.30205319 -1.43173593\n", " -1.47784682 -0.38873698 1.20197385 2.35758369 2.4033293 1.33339409\n", " -0.18562314 -1.21687133 -1.11843804 0.06938192 1.64835905 2.68350055\n", " 2.56705006 1.38508442 -0.13444119 -1.06085796 -0.82223292 0.44958982\n", " 1.99850132 2.9002282 2.61987956 1.33460187 -0.17329901 -0.98829474\n", " -0.61393553 0.72765888 2.22978557 2.98779512 2.54481636 1.16736204\n", " -0.31544305 -1.01202613 -0.50620012 0.89178 2.33238713 2.93930863\n", " 2.33809309 0.88205263 -0.56085455 -1.13159852 -0.49826434 0.94374399\n", " 2.3102546 2.76172856 2.00973769 0.49098778 -0.89609186 -1.33329087\n", " -0.57616702 0.89853024 2.18049461 2.47504112 1.5825372 0.01887107\n", " -1.29571509 -1.59171638 -0.71452375 0.78235267 1.97123209 2.1099297\n", " 1.08952395 -0.49989026 -1.72512787 -1.87280792 -0.87965142 0.62939344\n", " 1.7181973 1.70421498 0.57027654 -1.02500769 -2.14450529 -2.137838\n", " -1.03367474 0.47760828 1.46044012 1.29848001 0.06646661 -1.51482552\n", " -2.51334999 -2.34800292 -1.1391332 0.3640957 1.23567343 0.93139163\n", " -0.38282981 -1.93100089 -2.79514459 -2.46903474 -1.16354794 0.32057336\n", " 1.07579109 0.63526422 -0.74550978 -2.24280425 -2.96155738 -2.47530093\n", " -1.08341299 0.36949224 1.00308503 0.43238392 -1. -2.43053938\n", " -2.99571025 -2.35291175 -0.88714229 0.52124465 1.02760444 0.33252192\n", " -1.13757835 -2.4876847 -2.89412705 -2.10147081 -0.57662604 0.77279405\n", " 1.14596598 0.33192635 -1.1631911 -2.4215072 -2.66713509 -1.73426303\n", " -0.16721248 1.1078885 1.34175427 0.41390957 -1.0946721 -2.25207847\n", " -2.3376726 -1.27685668 0.31388467 1.49883394 1.58746591 0.55096047\n", " -0.96045719 -2.00980935 -1.93864172 -0.76428128 0.83056896 1.90962138\n", " 1.84776934 0.70813353 -0.796063 -1.73179237 -1.50911587 -0.23710996\n", " 1.34188723 2.30004343 2.08369917 0.84731657 -0.63974373 -1.45738083\n", " -1.08984464 0.26309767]\n" ] } ], "source": [ "sampling_freq = 1024 # Sampling frequency \n", "duration = 0.3 # 0.3 seconds of signal\n", "freq1 = 7 # 7 Hz signal\n", "freq2 = 130 # 130 Hz signal\n", "\n", "# Generate a time vector\n", "time_vector = np.arange(0, duration, 1/sampling_freq)\n", "\n", "# Generate a sine wave\n", "signal_1 = np.sin(2 * np.pi * freq1 * time_vector) \n", "# Generate another sine wave, with double the power\n", "signal_2 = np.sin(2 * np.pi * freq2 * time_vector) * 2 \n", " \n", "# Add the signals we created above into one signal\n", "combined_signal = signal_1 + signal_2\n", "\n", "print('You\\'ve created a complex signal with two sin waves, it looks like this:')\n", "print(combined_signal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we have are the signal values for our complex signal composed of the two sin waves. To see our `combined_signal` we must plot it using `plt.plot()`." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Set up our figure\n", "fig = plt.figure(figsize=(12, 4))\n", "\n", "# Plot 0.5 seconds of data\n", "plt.plot(time_vector, combined_signal) \n", "plt.ylabel('Signal',fontsize=14)\n", "plt.xlabel('Time',fontsize=14)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# plt.plot(time_vector, signal_1)\n", "# plt.show()\n", "# plt.plot(time_vector, signal_2)\n", "# plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we'll calculate the **Fourier Transform**, which will determine the frequencies that are in our sample. We'll implement this with Welch's Method, which consists in averaging consecutive Fourier transform of small windows of the signal, with or without overlapping. Basically, we calculate the fourier transform of a signal across a few sliding windows, and then calculate the mean PSD from all the sliding windows.\n", "\n", "The freqs vector contains the x-axis (frequency bins) and the psd vector contains the y-axis values (power spectral density). The units of the power spectral density, when working with EEG data, is usually $\\mu$V^2 per Hz." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/VictorMagdaleno/opt/anaconda3/lib/python3.7/site-packages/scipy/signal/spectral.py:1966: UserWarning: nperseg = 4096 is greater than input length = 308, using nperseg = 308\n", " .format(nperseg, input_length))\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Import signal processing module \n", "from scipy import signal\n", "\n", "# Define sliding window length (4 seconds, which will give us 2 full cycles at 0.5 Hz)\n", "win = 4 * sampling_freq\n", "freqs, psd = signal.welch(combined_signal, sampling_freq, nperseg=win)\n", "\n", "# Plot our data\n", "plt.plot(freqs,psd) # Plot a select range of frequencies\n", "plt.ylabel('Power')\n", "plt.xlabel('Frequency (Hz)')\n", "plt.title('FFT of a complex signal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Fourier Transformation shows us the signal frequencies that make up our combined signal. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SciPy for Signal Procesing " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Normal physiological data is never as regular as the data above -- it's usually chock full of lots of different waves, as well as noise. Now that we have a sense of the tools we need, let's work with some real data.\n", "\n", "The data we'll import here is a real 30-seconds extract of slow-wave sleep from a young individual, collected by the Walker Lab at UC Berkeley. This data was collected at 100 Hz from channel 'F3'. This sampling frequency is fine for EEG data, but wouldn't be enough for high frequency spiking data. That kind of data is typically sampled at 40 kHz." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "import urllib.request\n", "\n", "# URL of data to download\n", "data_url = 'https://raphaelvallat.com/images/tutorials/bandpower/data.txt'\n", "\n", "# Get the data and save it locally as \"sleep_data.txt\"\n", "sleep_data, headers = urllib.request.urlretrieve(data_url, './sleep_data.txt')\n", "\n", "# Load the .txt file as a numpy array\n", "data = np.loadtxt('sleep_data.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the data, let's took a look at the raw signal." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sampling_freq = 100\n", "\n", "time_vector = np.arange(0,30,1/sampling_freq)\n", "fig = plt.figure(figsize=(15,5))\n", "plt.plot(time_vector,data)\n", "plt.xlabel('Time (s)')\n", "plt.ylabel('Voltage (V)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this real EEG data, the underlying frequencies are much harder to see by eye. So, we'll compute a bandpass by first applying a low-pass filter_, followed by a _high-pass filter (or vice versa)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Signal filtration is usually accomplished in 2 steps\n", "1. Design a _filter kernel_\n", "2. Apply the filter kernel to the data\n", " \n", "We will use a **Butterworth** filter. The ideal filter would _completely_ pass everything in the passband (i.e., allow through the parts of the signal we care about) and completely reject everything outside of it, but this cannot be achieved in reality—the Butterworth filter is a close approximation.\n", "\n", "We design the filter in Python using `scipy`'s `signal.butter` function, with three arguments:\n", "1. The _filter order_ : (we'll use a 4th order filter)\n", "2. The _filter frequency_ : (we must adjust for the sampling frequency, `f_s`, which is 100 Hz for these data, i.e. 100 data points were recorded per second)\n", "3. The type of filter : (`'lowpass'` or `'highpass'`)\n", "\n", "It returns 2 filter parameters, `a` and `b`. Then, the bandpass filter is applied using `signal.filtfilt`, which takes as its parameters `b`, `a`, and the signal to be processed\n", "\n", "Below, an example bandpass computation is shown to extract the _alpha_ rhythm from the channel 1 data, the results are stored in a dictionary called `oscillations_filtered`, with the oscillation name (e.g. `'alpha'`) as the key" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# Define lower and upper limits of our bandpass\n", "filter_limits = [0.5, 4]\n", "\n", "# First, apply a lowpass filter\n", "# Design filter with high filter limit\n", "b, a = signal.butter(4, (filter_limits[1]/ (sampling_freq / 2)), 'lowpass') \n", "\n", "# Apply it forwards and backwards (filtfilt)\n", "lowpassed = signal.filtfilt(b, a, data)\n", "\n", "# Then, apply a high pass filter\n", "# Design filter with low filter limit\n", "b, a = signal.butter(4, (filter_limits[0] / (sampling_freq / 2)), 'highpass') \n", "\n", "# Apply it\n", "bandpassed = signal.filtfilt(b, a, lowpassed) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets plot our bandpassed data." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the bandpassed data\n", "fig = plt.figure(figsize=(15,5))\n", "plt.plot(time_vector,bandpassed)\n", "plt.ylabel('Voltage')\n", "\n", "# Let's programmatically set the title here, using {} format\n", "plt.title('N3 sleep EEG data (F3), {} band' .format(filter_limits))\n", "\n", "plt.xlabel('Time (seconds)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additional Resources" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }