├── .gitignore
├── 00-Introduction.ipynb
├── 01-ElectrophysiologicalTimeSeries.ipynb
├── 02-Filtering.ipynb
├── 03-SpectralMethods.ipynb
├── 04-EventRelatedPotentials.ipynb
├── 05-FrequencyInteractions.ipynb
├── 06-ColoredNoise.ipynb
├── 07-CommonMisinterpretations.ipynb
├── 08-FrequencyMisinterpretations.ipynb
├── README.md
├── dat
├── emodat.mat
├── emodat.npy
├── lfp_example.mat
├── sample_data_1.npy
└── sample_data_2.npy
├── img
├── HowPowerSpectraGoWrong.jpg
├── binder_logo.png
├── examplePSD.png
├── github_logo.png
├── image1.png
├── image2.jpg
├── image2.png
├── jupyter_logo.png
└── sky5.en.png
└── utils
└── plts.py
/.gitignore:
--------------------------------------------------------------------------------
1 | # Ignore nb checkpoints
2 | .ipynb_checkpoints/*
3 |
4 | # Ignore cache files
5 | */__pycache__/*
6 |
7 | # Ignore mac directory files
8 | .DS_Store
9 |
--------------------------------------------------------------------------------
/00-Introduction.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## Introduction"
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "
\n",
15 | "Welcome to the Voytek Lab GitHub and Jupyter notebook collection.\n",
16 | "This is the first Jupyter notebook in a tutorial series that discusses the main topics involved with the projects being done in Voytek lab. We will walk through many of the concepts and calculations that are used in our lab.\n",
17 | "
"
18 | ]
19 | },
20 | {
21 | "cell_type": "markdown",
22 | "metadata": {},
23 | "source": [
24 | "Knowledge in python is ***strongly*** recommended to help understand these tutorials and to help you preform these procedures, although it is not strictly necessary. Here are a couple of links that cover Python basics, from installation to more complex topics. These are resources and tutorials from two classes the lab has developed here at UC San Diego for the [Department of Cognitive Science](http://www.cogsci.ucsd.edu/) and the [Halıcıoğlu Data Science Institute](http://datascience.ucsd.edu/) and undergraduate Data Science major.
\n",
25 | "\n",
26 | "- [COGS 18: Introduction to Python](https://cogs18.github.io/intro/)
\n",
27 | "- [COGS 108: Data Science in Practice](https://cogs108.github.io/Tutorials/00-Introduction)
"
28 | ]
29 | },
30 | {
31 | "cell_type": "markdown",
32 | "metadata": {},
33 | "source": [
34 | "The purpose of _this_ notebook is to:\n",
35 | "\n",
36 | "- Introduce you to Jupyter Notebook.\n",
37 | "- Learn how to run Notebook cells.\n",
38 | "- Install relevant Python packages.\n",
39 | "- Load your data into a Jupyter Notebook."
40 | ]
41 | },
42 | {
43 | "cell_type": "markdown",
44 | "metadata": {},
45 | "source": [
46 | "This notebook will be a brief introduction and we encourage you to explore these tools further. "
47 | ]
48 | },
49 | {
50 | "cell_type": "markdown",
51 | "metadata": {},
52 | "source": [
53 | "
"
54 | ]
55 | },
56 | {
57 | "cell_type": "markdown",
58 | "metadata": {},
59 | "source": [
60 | "### Setting up Jupyter Notebooks"
61 | ]
62 | },
63 | {
64 | "cell_type": "markdown",
65 | "metadata": {},
66 | "source": [
67 | "\n",
68 | "Jupyter Notebooks are an easy way to develop algorithms through which we can process our data. It lets us write, run, and present code and results in an approachable format.\n",
69 | "
"
70 | ]
71 | },
72 | {
73 | "cell_type": "markdown",
74 | "metadata": {},
75 | "source": [
76 | "Anaconda is a Python distribution that manages Python packages and environments. Jupyter Notebooks are included with Anaconda, but are separate. \n",
77 | "\n",
78 | "Below are the two recommended ways to consume these tutorials. If you think you will be using Jupyter in the future, we recommend following the first option. Otherwise if you just want to take a look at these tutorials and never return to Jupyter, you should probably use the second option, which will lead you to a cloud-based environment that allows you to run these tutorial notebooks without installing anything on your system.
\n",
79 | "\n",
80 | "#### Follow along using Git\n",
81 | "\n",
82 | "
\n",
83 | "\n",
84 | "- Download Anaconda [here](https://store.continuum.io/cshop/anaconda/).\n",
85 | "\n",
86 | "- Clone this [repository](https://github.com/voytekresearch/tutorials) by using git clone.\n",
87 | "\n",
88 | "- Run the 'Anaconda Navigator' GUI from first bullet point and launch this Jupyter notebook.\n",
89 | "\n",
90 | "- Navigate to where you cloned the GitHub repository and click on the _.ipynb_ file you want to run.
\n",
91 | "\n",
92 | "#### Follow along using Binder\n",
93 | "\n",
94 | "
\n",
95 | "\n",
96 | "Alternatively, if you don't want to install anything open this [Binder link](https://mybinder.org/v2/gh/voytekresearch/tutorials/master). You can consume 100% of these tutorials this way without installing anything on your local machine."
97 | ]
98 | },
99 | {
100 | "cell_type": "markdown",
101 | "metadata": {},
102 | "source": [
103 | "\n",
104 | "If you are unfamiliar with Jupyter Notebooks, follow the above links for COGS 18 and COGS 108.\n",
105 | "
\n",
106 | "\n"
107 | ]
108 | },
109 | {
110 | "cell_type": "markdown",
111 | "metadata": {},
112 | "source": [
113 | "## Modules"
114 | ]
115 | },
116 | {
117 | "cell_type": "markdown",
118 | "metadata": {},
119 | "source": [
120 | "\n",
121 | "In this section we will discuss the Python modules used most often in Voytek lab, and show you how to install them.\n",
122 | "
"
123 | ]
124 | },
125 | {
126 | "cell_type": "markdown",
127 | "metadata": {},
128 | "source": [
129 | "#### Installing modules using Anaconda\n",
130 | "If you are using Anaconda, you will be able to install many modules by opening 'Anaconda Prompt' and running the command: pip install <*module name*>
\n",
131 | "\n",
132 | "\n",
133 | "For example:
\n",
134 | "$pip install pandas
\n",
135 | "_pip_ is a package installation tool. On the other hand if you are using Binder to follow along then everything should be set up already."
136 | ]
137 | },
138 | {
139 | "cell_type": "code",
140 | "execution_count": 14,
141 | "metadata": {},
142 | "outputs": [],
143 | "source": [
144 | "# this sets up the notebook to allow inline plotting of higher-resolution plots\n",
145 | "%matplotlib inline\n",
146 | "%config InlineBackend.figure_format = 'retina' \n",
147 | "\n",
148 | "# here are some common modules used in Voytek lab:\n",
149 | "import numpy as np # package for scientific computing in python\n",
150 | "import scipy as sp # package of scientific data functions\n",
151 | "import pandas as pd # package of data analysis functions\n",
152 | "import sklearn as skl # package for data mining and machine learning\n",
153 | "\n",
154 | "import matplotlib.pyplot as plt # functions to plot data\n",
155 | "\n",
156 | "# here are some plotting set-ups\n",
157 | "from matplotlib import rcParams\n",
158 | "rcParams['figure.figsize'] = 16, 4"
159 | ]
160 | },
161 | {
162 | "cell_type": "markdown",
163 | "metadata": {},
164 | "source": [
165 | "### Importing Data"
166 | ]
167 | },
168 | {
169 | "cell_type": "markdown",
170 | "metadata": {},
171 | "source": [
172 | "Let's start by loading some electrophysiology data. To do that, we need to tell python where to get the data from and tell it to save it as a variable.
\n",
173 | "'emodat.npy' is data from one channel of an [ECoG recording](https://en.wikipedia.org/wiki/Electrocorticography) from a participant looking at faces displaying various emotions. \n",
174 | "\n",
175 | "We'll use this data in several of the tutorials. It should be saved at './dat/emodat.npy'"
176 | ]
177 | },
178 | {
179 | "cell_type": "code",
180 | "execution_count": 15,
181 | "metadata": {
182 | "collapsed": true
183 | },
184 | "outputs": [],
185 | "source": [
186 | "# The name of the data file to load\n",
187 | "filename = \"./dat/emodat.npy\"\n",
188 | "\n",
189 | "# Loading data\n",
190 | "data = np.load(filename)"
191 | ]
192 | },
193 | {
194 | "cell_type": "markdown",
195 | "metadata": {},
196 | "source": [
197 | "Let's use our plotting library to plot some of the data."
198 | ]
199 | },
200 | {
201 | "cell_type": "code",
202 | "execution_count": 16,
203 | "metadata": {},
204 | "outputs": [
205 | {
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80tCaWIoviM38PcKJPIJYAACAdMusiG1vxAiptta6ZjU2lndeAAAAQE9AEJtC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utCrBAJJ+GKoKcwXYgFpDyxySTjiAWUEPvhh+4YL4KC48e8L4wWop04YquqVIwmG3SkIIRYdsSK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208 | "text/plain": [
209 | ""
210 | ]
211 | },
212 | "metadata": {
213 | "image/png": {
214 | "height": 255,
215 | "width": 945
216 | }
217 | },
218 | "output_type": "display_data"
219 | }
220 | ],
221 | "source": [
222 | "plt.plot(data[0:1000]) #this is what about 1 second of ECoG data looks like\n",
223 | "plt.show()"
224 | ]
225 | },
226 | {
227 | "cell_type": "markdown",
228 | "metadata": {},
229 | "source": [
230 | "Alright, that's all we're going to talk about here! Good luck with Jupyter Notebook and the rest of the tutorials."
231 | ]
232 | },
233 | {
234 | "cell_type": "code",
235 | "execution_count": null,
236 | "metadata": {
237 | "collapsed": true
238 | },
239 | "outputs": [],
240 | "source": []
241 | }
242 | ],
243 | "metadata": {
244 | "kernelspec": {
245 | "display_name": "Python 3",
246 | "language": "python",
247 | "name": "python3"
248 | },
249 | "language_info": {
250 | "codemirror_mode": {
251 | "name": "ipython",
252 | "version": 3
253 | },
254 | "file_extension": ".py",
255 | "mimetype": "text/x-python",
256 | "name": "python",
257 | "nbconvert_exporter": "python",
258 | "pygments_lexer": "ipython3",
259 | "version": "3.5.3"
260 | }
261 | },
262 | "nbformat": 4,
263 | "nbformat_minor": 2
264 | }
265 |
--------------------------------------------------------------------------------
/01-ElectrophysiologicalTimeSeries.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Electrophysiological Time-Series"
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "**Michael Tran 2018**\n",
15 | "\n",
16 | "Welcome to the Voytek Lab GitHub and Jupyter notebook collection. This is the second Jupyter notebook of a tutorial series aiming to get those interested in doing meaningful work in Voytek lab up to speed. We use this tool to learn how to load our data and graph it.\n",
17 | "\n",
18 | "The purpose of this notebook is to:\n",
19 | "* Learn how to load data with a npy file \n",
20 | "* Learn the purpose of Sampling and Aliasing\n",
21 | "* Generate a time axis and plot the time series as a graph\n",
22 | "* Learn (generally) what our generated graph shows\n",
23 | "* Learn about different wave types and burstiness\n",
24 | "* Introduce you to reference nodes\n"
25 | ]
26 | },
27 | {
28 | "cell_type": "markdown",
29 | "metadata": {},
30 | "source": [
31 | "### Default Imports"
32 | ]
33 | },
34 | {
35 | "cell_type": "markdown",
36 | "metadata": {},
37 | "source": [
38 | "First, we will import some basic python libraries that contain functions in order to do this tutorial."
39 | ]
40 | },
41 | {
42 | "cell_type": "code",
43 | "execution_count": 2,
44 | "metadata": {
45 | "collapsed": true
46 | },
47 | "outputs": [],
48 | "source": [
49 | "\n",
50 | "import numpy as np\n",
51 | "import scipy as sp\n",
52 | "import matplotlib.pyplot as plt\n",
53 | "\n",
54 | "import seaborn as sns\n",
55 | "sns.set_style('white')"
56 | ]
57 | },
58 | {
59 | "cell_type": "markdown",
60 | "metadata": {},
61 | "source": [
62 | "## Part 1: Loading, Sampling, Generating a time axis, Analyzing"
63 | ]
64 | },
65 | {
66 | "cell_type": "markdown",
67 | "metadata": {},
68 | "source": [
69 | "### Loading data with npy file"
70 | ]
71 | },
72 | {
73 | "cell_type": "markdown",
74 | "metadata": {},
75 | "source": [
76 | "We will now load in our data which is a voltage series."
77 | ]
78 | },
79 | {
80 | "cell_type": "code",
81 | "execution_count": 3,
82 | "metadata": {},
83 | "outputs": [],
84 | "source": [
85 | "volt = np.load('./dat/sample_data_2.npy') # voltage series"
86 | ]
87 | },
88 | {
89 | "cell_type": "markdown",
90 | "metadata": {},
91 | "source": [
92 | "### Sampling & Aliasing"
93 | ]
94 | },
95 | {
96 | "cell_type": "markdown",
97 | "metadata": {},
98 | "source": [
99 | "In order to generate our time axis, we must set a sampling rate. What does this mean you might ask? Jack Schaedler at [his interactive tutorial page](https://jackschaedler.github.io/circles-sines-signals/aliasing.html) does an excellent job of explaining this under \"Sampling & Aliasing\", so read up and return!\n",
100 | "\n",
101 | "-----------------------------------------------------------------------------------------------------------------------------\n",
102 | "\n",
103 | "To go further in depth, the sampling rate is important for how we analyze data in the frequency domain. When you digitally record a signal, you measure it at a certain rate by taking samples x times per second (quantified in Hertz). For this reason, we don't know the exact amplitude of any digital signal at every single point in time. Our best guess is the closest sample to a given point in time.\n",
104 | "\n",
105 | "\n",
106 | "Resampling, PSD, and many other things we do in this lab fall under the title of Digital Signal Processing. You can read more about it for free [here](http://dspguide.com/)\n",
107 | "\n",
108 | "##### So why should we Sample?\n",
109 | "\n",
110 | "We sample because there is a cost to data which is memory. If we have a faster sampling rate, we can have better temporal resolution but storing excessive samples can make the file size too big and use too much memory. Sometimes you will have extremely large data sets that simply take too much memory so we take sample rates to divide that space usage. It is possible to oversample which means that we have more samples than necessary which would make our files unneccesarily large, but there is also undersampling which is when we lose information by not sampling enough, but in return we can make our files smaller. The sample rate is something that we must choose carefully as there is a trade off for both sides.\n",
111 | "\n",
112 | "-----------------------------------------------------------------------------------------------------------------------------\n",
113 | "If you would like further information on how to choose a good sampling rate, take a look at Schaedler's interactive site https://jackschaedler.github.io/circles-sines-signals/sampling.html under \"The Sampling Theorem\"\n",
114 | "\n"
115 | ]
116 | },
117 | {
118 | "cell_type": "markdown",
119 | "metadata": {},
120 | "source": [
121 | "To show how sampling can go wrong if we jump to conclusions, take a look at figure 1 shown below. \n",
122 | "\n",
123 | "We have four different 2 hour flights from Paris to Berlin with a sample rate of 10 minutes. We should take care not to \"connect the dots\" as we can see that figure 1 has the same sample rates for four different flights which all create the same graph (shown in grey). We can see here, however, that they are drastically different altitude patterns when we see the overall flight altitudes of each flight."
124 | ]
125 | },
126 | {
127 | "cell_type": "markdown",
128 | "metadata": {},
129 | "source": [
130 | "\n",
131 | "from [Jack Schaedler](https://jackschaedler.github.io/circles-sines-signals/aliasing.html)"
132 | ]
133 | },
134 | {
135 | "cell_type": "markdown",
136 | "metadata": {},
137 | "source": [
138 | "### Generate time axis"
139 | ]
140 | },
141 | {
142 | "cell_type": "markdown",
143 | "metadata": {},
144 | "source": [
145 | "Now, we will generate our time axis. We will set our sampling rate to be 1000 in this example case."
146 | ]
147 | },
148 | {
149 | "cell_type": "code",
150 | "execution_count": 4,
151 | "metadata": {
152 | "collapsed": true
153 | },
154 | "outputs": [],
155 | "source": [
156 | "rate = 1000. # sampling rate\n",
157 | "time = np.arange(0,len(volt)/rate, 1/rate) #time series"
158 | ]
159 | },
160 | {
161 | "cell_type": "markdown",
162 | "metadata": {},
163 | "source": [
164 | "### 1. Plot the time series"
165 | ]
166 | },
167 | {
168 | "cell_type": "markdown",
169 | "metadata": {},
170 | "source": [
171 | "We have our time series and now we will visualize it by graphing it."
172 | ]
173 | },
174 | {
175 | "cell_type": "code",
176 | "execution_count": 5,
177 | "metadata": {},
178 | "outputs": [
179 | {
180 | "data": {
181 | "image/png": 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CAwNx9+5dnD17Fnp6ehg8eDAAwNXVFampqa1y7FWrVgEAampqsGTJEiYhlpRkTk9P1/gY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182 | "text/plain": [
183 | ""
184 | ]
185 | },
186 | "metadata": {},
187 | "output_type": "display_data"
188 | }
189 | ],
190 | "source": [
191 | "time_plot = [9, 10.5] # time range you want to plot\n",
192 | "time_plot_idx = np.where(np.logical_and(time>=time_plot[0], time < time_plot[1]))[0]\n",
193 | "plt.figure(figsize=(12,3))\n",
194 | "plt.plot(time[time_plot_idx], volt[time_plot_idx],'k')\n",
195 | "plt.yticks(size=15)\n",
196 | "plt.xticks(size=15)\n",
197 | "plt.xlabel('Time (s)',size=20)\n",
198 | "plt.ylabel('Voltage (uV)',size=20)\n",
199 | "plt.xlim((time_plot))\n",
200 | "plt.show()"
201 | ]
202 | },
203 | {
204 | "cell_type": "markdown",
205 | "metadata": {},
206 | "source": [
207 | "##### Analyzing our graph\n",
208 | "Neural data has rythmic patterns that are variable as well as other patterns that are \"bursty\" i.e non continuous shown above. Our graph also has 8 peaks in half a second which is equal to 16 Hz, making this a Beta wave.\n",
209 | "\n",
210 | "Different neural oscillatory patterns have different ranges and the general ranges are shown below."
211 | ]
212 | },
213 | {
214 | "cell_type": "markdown",
215 | "metadata": {},
216 | "source": [
217 | "\n",
218 | "from [mindbodyvortex](http://www.mindbodyvortex.com/wp-content/uploads/2015/09/f3a6b3eb4cc6f2d7392b34284c233281.jpg)"
219 | ]
220 | },
221 | {
222 | "cell_type": "markdown",
223 | "metadata": {},
224 | "source": [
225 | "## Part 2: Ground vs. Referencing"
226 | ]
227 | },
228 | {
229 | "cell_type": "markdown",
230 | "metadata": {},
231 | "source": [
232 | "To learn more about ground vs. reference for EEG recordings, we can visit \n",
233 | "1. https://www.biopac.com/knowledge-base/ground-vs-reference-for-eeg-recording/\n",
234 | "2. (https://www.researchgate.net/post/What_is_the_difference_between_ground_and_reference_electrode_in_EEG_recording)\n",
235 | "\n",
236 | "To summarize,\n",
237 | "\n",
238 | "An EEG (electroencephalogram)is the potential difference between an active electrode and a reference electrode.\n",
239 | "Voltage signals are always relative. You can have different references.\n",
240 | "\n",
241 | "The ground electrode is used to reduce artifacts (electrical circuit, mouvement...), preventing noise from interfering with the signals of interest.\n",
242 | "\n",
243 | "Referencing is when you have an electrode you use to measure the electrical difference between the active electrode and itself.\n"
244 | ]
245 | },
246 | {
247 | "cell_type": "markdown",
248 | "metadata": {},
249 | "source": [
250 | "\"The reference lead is the lead that connects the reference electrode; in EEG recordings, this electrode is usually placed at the ear or, in the case of “summed ears,” to a pair of electrodes, one at each ear. The measured electrical potential differences are ideally the voltage drops from the active electrode to the reference electrode.\n"
251 | ]
252 | },
253 | {
254 | "cell_type": "markdown",
255 | "metadata": {},
256 | "source": [
257 | "## Part 3: Neural Oscillations"
258 | ]
259 | },
260 | {
261 | "cell_type": "markdown",
262 | "metadata": {},
263 | "source": [
264 | "# UNDER CONSTRUCTION"
265 | ]
266 | }
267 | ],
268 | "metadata": {
269 | "kernelspec": {
270 | "display_name": "Python 3",
271 | "language": "python",
272 | "name": "python3"
273 | },
274 | "language_info": {
275 | "codemirror_mode": {
276 | "name": "ipython",
277 | "version": 3
278 | },
279 | "file_extension": ".py",
280 | "mimetype": "text/x-python",
281 | "name": "python",
282 | "nbconvert_exporter": "python",
283 | "pygments_lexer": "ipython3",
284 | "version": "3.6.2"
285 | }
286 | },
287 | "nbformat": 4,
288 | "nbformat_minor": 2
289 | }
290 |
--------------------------------------------------------------------------------
/README.md:
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1 | # Tutorials
2 |
3 | 
4 | [](http://mybinder.org/repo/voytekresearch/tutorials)
5 |
6 | A set of tutorials for getting started with the VoytekLab.
7 |
8 | ## Overview
9 |
10 | The tutorials here are aimed at a general overview of analysing electrophysiological data.
11 |
12 | - Getting Started with Python & Jupyter
13 | - Electrophysiological Time Series
14 | - Filtering
15 | - Spectral Estimation
16 | - Event-Related Analyses
17 | - Frequency Interactions
18 | - Time Series Properties
19 | - Noise, Filters, Bands
20 | - Time Domain Analyses
21 |
22 | For more advanced materials, check out [specparam](https://github.com/fooof-tools/fooof),
23 | our module to parameterize neural power spectra, which has specific tutorials
24 | [here](https://fooof-tools.github.io/fooof/) and/or
25 | [NeuroDSP](https://github.com/neurodsp-tools/neurodsp),
26 | our module focused on time-domain analyses, which has tutorials
27 | [here](https://neurodsp-tools.github.io/neurodsp/).
28 |
29 | ## Dependencies
30 |
31 | All the notebooks are written in Python3, and presume that you have the
32 | [Anaconda](https://www.anaconda.com/download/) distribution.
33 |
34 | ## Try Them Out
35 |
36 | If you want to jump right in to executable notebooks, running on the cloud, press
37 | [here](http://mybinder.org/repo/voytekresearch/tutorials)(powered by [Binder](https://mybinder.org)).
38 |
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/img/HowPowerSpectraGoWrong.jpg:
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/utils/plts.py:
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1 | """Tools and utilities for plotting."""
2 |
3 | import matplotlib.pyplot as plt
4 |
5 | from fooof.plts.templates import plot_spectrum
6 |
7 | ###################################################################################################
8 | ###################################################################################################
9 |
10 | def plot_fm_shading(fm, cens, bws):
11 | """Plot a FOOOF power spectrum with shaded regions.
12 |
13 | Parameters
14 | ----------
15 | fm : FOOOF() object
16 | FOOOF object with power spectrum data to plot.
17 | cens : list of float
18 | List of center values to shade around.
19 | bws : list of float
20 | List of ranges to plot +/- each center regions.
21 | """
22 |
23 | plot_spectrum(fm.freqs, fm.power_spectrum)
24 |
25 | # Add shading +/- BW around each provided CEN.
26 | # Note: m is a potential scaling of BW. Currently not-exposed (hard set to 1).
27 | m = 1
28 | for cen, bw in zip(cens, bws):
29 | plt.axvspan(cen, cen, color='g')
30 | plt.axvspan(cen-(m*bw), cen+(m*bw), color='r', alpha=0.2, lw=0)
31 |
32 |
33 | def noise_time_plot(data, time_scale):
34 |
35 | #plot 1 second of pink noise data
36 | plt.figure(figsize = [12,6])
37 | plt.plot(time_scale[0:1000], data[0:1000]) #plot samples over time
38 |
39 | def noise_frequency_plot(fourier, fx_bins, title):
40 | # calculating fourier transform
41 | # we're going to take a sample of the data to keep fx bins at a reasonable size.
42 |
43 | plt.figure(figsize=(16,10))
44 | plt.subplot(1,2,1)
45 | plt.plot(fx_bins[0:1500],abs(fourier[0:1500]))
46 | plt.ylabel('Power')
47 | plt.xlabel('Frequency (Hz)')
48 | plt.title(title)
49 |
50 | #same thing but in log space
51 | plt.subplot(1,2,2)
52 | plt.plot(fx_bins[0:1500],np.log(abs(fourier[0:1500])))
53 | plt.ylabel('log Power')
54 | plt.xlabel('Frequency (Hz)')
55 | plt.title(title)
56 |
57 | def welch_plot(fs, ps, title):
58 | #Welch's PSD of brown noise
59 |
60 | plt.figure(figsize=(16,10))
61 | plt.loglog(fs[0:200*2],ps[0:200*2])
62 | plt.ylabel('Power')
63 | plt.xlabel('Frequency (Hz)')
64 | plt.xlim([1, 150])
65 | plt.title(title)
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