{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lesson 28: Plotting time series, generated data, and ECDFs\n", "\n", "(c) 2018 Justin Bois. With the exception of pasted graphics, where the source is noted, this work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT).\n", "\n", "This document was prepared at [Caltech](http://www.caltech.edu) with financial support from the [Donna and Benjamin M. Rosen Bioengineering Center](http://rosen.caltech.edu).\n", "\n", "\n", "\n", "*This lesson was generated from a Jupyter notebook. You can download the notebook [here](l28_plotting_time_series_generated_data_and_ecdfs.ipynb).*\n", "\n", "

" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import scipy.special\n", "\n", "import altair as alt\n", "\n", "import bootcamp_utils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting (long) time series data\n", "\n", "So far, we have seen how to plot measured data. One class of measured data we have not considered is time series data. Time series data are typically not plotted as points, but rather with joined lines. To get some experience plotting data sets like this, we will use some data from [Markus Meister](https://meisterlab.caltech.edu)'s group, collected by graduate students Dawna Bagherian and Kyu Lee. The file `~/git/data/retina_spikes.csv` contains the data set. They put electrodes in the retinal cells of a mouse and measured voltage. From the time trace of voltage, they can detect and characterize spiking events. The first column of the CSV file is the time in milliseconds (ms) that the measurement was taken, and the second column is the voltage in units of microvolts (µV). Let's load in the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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t (ms)V (uV)
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" ], "text/plain": [ " t (ms) V (uV)\n", "0 703.96 4.79\n", "1 704.00 -0.63\n", "2 704.04 5.83\n", "3 704.08 0.31\n", "4 704.12 -4.58" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/retina_spikes.csv', comment='#')\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make a plot of these data, we choose `mark_line()`. You may be tempted to specify that the data type of `x` is temporal. You really only want to do that if the data are time (like hours, minutes, seconds) and/or dates. You should still specify the data type of `x` to be quantitative." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "MaxRowsError", "evalue": "The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mMaxRowsError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/altair/vegalite/v2/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_prepare_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;31m# We make use of two context markers:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/altair/vegalite/v2/api.py\u001b[0m in \u001b[0;36m_prepare_data\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUrlData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m 550\u001b[0m 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"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 284\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m 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'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m'For information on how to plot larger datasets '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m 'in Altair, see the documentation'.format(max_rows))\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mMaxRowsError\u001b[0m: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation" ] }, { "data": { "text/plain": [ "Chart({\n", " data: t (ms) V (uV)\n", " 0 703.96 4.79\n", " 1 704.00 -0.63\n", " 2 704.04 5.83\n", " 3 704.08 0.31\n", " 4 704.12 -4.58\n", " 5 704.16 21.67\n", " 6 704.20 8.13\n", " 7 704.24 5.94\n", " 8 704.28 14.79\n", " 9 704.32 2.71\n", " 10 704.36 0.94\n", " 11 704.40 17.40\n", " 12 704.44 11.77\n", " 13 704.48 -2.60\n", " 14 704.52 6.04\n", " 15 704.56 -3.44\n", " 16 704.60 -6.04\n", " 17 704.64 10.42\n", " 18 704.68 19.06\n", " 19 704.72 7.40\n", " 20 704.76 9.79\n", " 21 704.80 20.42\n", " 22 704.84 -0.10\n", " 23 704.88 -7.92\n", " 24 704.92 -7.29\n", " 25 704.96 -11.25\n", " 26 705.00 -12.19\n", " 27 705.04 12.60\n", " 28 705.08 -1.98\n", " 29 705.12 -10.31\n", " ... ... ...\n", " 19971 1502.80 -11.04\n", " 19972 1502.84 -2.92\n", " 19973 1502.88 -6.46\n", " 19974 1502.92 -11.98\n", " 19975 1502.96 -2.40\n", " 19976 1503.00 -2.81\n", " 19977 1503.04 -16.67\n", " 19978 1503.08 -5.94\n", " 19979 1503.12 9.38\n", " 19980 1503.16 4.69\n", " 19981 1503.20 4.58\n", " 19982 1503.24 6.15\n", " 19983 1503.28 -2.71\n", " 19984 1503.32 -5.63\n", " 19985 1503.36 10.31\n", " 19986 1503.40 -3.96\n", " 19987 1503.44 -6.56\n", " 19988 1503.48 6.67\n", " 19989 1503.52 -2.50\n", " 19990 1503.56 -5.00\n", " 19991 1503.60 15.63\n", " 19992 1503.64 7.19\n", " 19993 1503.68 -2.92\n", " 19994 1503.72 8.33\n", " 19995 1503.76 -1.56\n", " 19996 1503.80 -4.27\n", " 19997 1503.84 17.50\n", " 19998 1503.88 9.48\n", " 19999 1503.92 -5.94\n", " 20000 1503.96 -1.56\n", " \n", " [20001 rows x 2 columns],\n", " encoding: EncodingWithFacet({\n", " x: X({\n", " shorthand: 't (ms):Q'\n", " }),\n", " y: Y({\n", " shorthand: 'V (uV):Q',\n", " title: 'V (µV)'\n", " })\n", " }),\n", " mark: 'line'\n", "})" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(df\n", " ).mark_line(\n", " ).encode(\n", " x='t (ms):Q',\n", " y=alt.Y('V (uV):Q', title='V (µV)'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wait! What happend? Altair threw an exception that the data set was too large to generate a plot. The [Altair docs](https://altair-viz.github.io/user_guide/faq.html#why-does-altair-lead-to-such-extremely-large-notebooks) explain why this is.\n", "\n", ">This is not because Altair cannot handle larger datasets, but it is because it is important for the user to think carefully about how large datasets are handled. As noted above in Why does Altair lead to such extremely large notebooks?, it is quite easy to end up with very large notebooks if you make many visualizations of a large dataset, and this error is a way of preventing that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handling large data sets with Altair\n", "\n", "You will likely bump up against this in your work if you have large data sets. The [docs](https://altair-viz.github.io/user_guide/faq.html#maxrowserror-how-can-i-plot-large-datasets) give several ways to handle large data sets. Passing by URL is your best option, in my opinion. All you need to do to enable this is to include this line of code at the top of your notebook.\n", "```python\n", "alt.data_transformers.enable('json')\n", "```\n", "After you do that, you just use exactly the same plotting code as you would without it, except now the large data sets are easily handled. If this is enabled, every time you make a plot, a new [JSON document](https://en.wikipedia.org/wiki/JSON)is created externally to your notebook. Altair then references this document This keeps your notebook size down and results in better performance. There are a couple of important watchouts for doing this, though.\n", "1. Every time you make a plot, or even re-run a cell to make the same plot again, a new JSON file is created. (Future versions of Altair will probably have hashes that will prevent it from making new files if re-making a plot.) This can result in lots of JSON files in your directory. You should occasionally clean these out.\n", "2. Apparently, the JupyterLab extension for Bokeh (which we will use later in the course) messes with the way JupyterLab routes URL paths, as described in [this issue on the Altair GibHub page](https://github.com/altair-viz/altair/issues/927) and [this one on the `jupyterlab_bokeh` page](https://github.com/bokeh/jupyterlab_bokeh/issues/42). I suspect this issue will be fixed soon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fix 1 for issue 2\n", "As a temporary fix for issue 2, you can reinstall JupyterLab without the Bokeh extension. To do that, do the following on the command line (**don't** do this for bootcamp; only if in the future you want to use Altair exclusively).\n", "\n", " jupyter labextension uninstall --no-build jupyterlab_bokeh\n", " jupyter labextension uninstall --no-build @ijmbarr/jupyterlab_spellchecker\n", " jupyter labextension uninstall --no-build @pyviz/jupyterlab_holoviews\n", " jupyter labextension uninstall @jupyter-widgets/jupyterlab-manager\n", " conda uninstall --yes jupyterlab\n", " conda install --yes jupyterlab\n", "\n", "You can reinstall the spell checking lab extension if you wish, using\n", "\n", " jupyter labextension install @ijmbarr/jupyterlab_spellchecker\n", " \n", "Your clean install of JupyterLab will work with URL referencing in Altair plots." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fix 2 for issue 2\n", "\n", "As another temporary fix, you can use classic Jupyter notebooks instead of JupyterLab. The classic notebook will soon be phased out, but for now, you can use Altair with it so long as you have Vega installed. To install Vega, do the following on the command line\n", "\n", " conda install vega\n", " \n", "You can launch your notebook using the Anaconda Launcher (open a Notebook, not JupyterLab). Right after your import of Altair, include the following lines to enable rendering of Altair plots in the classic notebook and URL passing.\n", "\n", "```python\n", "alt.renderers.enable('notebook')\n", "alt.data_transformers.enable('json')\n", "```\n", "\n", "Then you can proceed as normal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Trying again with a smaller data set\n", "\n", "Instead of implementing the fixes to use URL referencing and to keep the notebook size down, we will take a smaller data set. We will use `iloc` for purely integer indexing of the `DataFrame` and only consider 1000 data points." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "data": { "values": [ { "V (uV)": 4.79, "t (ms)": 703.96 }, { "V (uV)": -0.63, "t (ms)": 704 }, { "V (uV)": 5.83, "t (ms)": 704.04 }, { "V (uV)": 0.31, "t (ms)": 704.08 }, { "V (uV)": -4.58, "t (ms)": 704.12 }, { "V (uV)": 21.67, "t (ms)": 704.16 }, { "V (uV)": 8.13, "t (ms)": 704.2 }, { "V (uV)": 5.94, "t (ms)": 704.24 }, { "V (uV)": 14.79, "t (ms)": 704.28 }, { "V (uV)": 2.71, "t (ms)": 704.32 }, { "V (uV)": 0.94, "t (ms)": 704.36 }, { "V (uV)": 17.4, "t (ms)": 704.4 }, { "V (uV)": 11.77, "t (ms)": 704.44 }, { "V (uV)": -2.6, "t (ms)": 704.48 }, { "V (uV)": 6.04, "t (ms)": 704.52 }, { "V (uV)": -3.44, "t (ms)": 704.56 }, { "V (uV)": 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.iloc[:1000, :]\n", "\n", "alt.Chart(df\n", " ).mark_line(\n", " ).encode(\n", " x='t (ms):Q',\n", " y=alt.Y('V (uV):Q', title='V (µV)'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see a spike clearly resolved." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Encoding the order\n", "\n", "Remember that there is no inherent order to the entries in a `DataFrame`. (Note that when we do this, `iloc` can be really dangerous to use. In general, you really should avoid using `iloc`.) Altair tries to be clever when making line plots as to the order in which the points are connected. Specifically, it assumes that the lines should be connected such that the `x` values are sorted. This often works fine, but what if we wanted to plot the trace with time on the y-axis and voltage on the x-axis? Let's try it. Before we do, though, I'll point out that by default, Altair starts the y-axis at zero if all of the y-values are positive. If we want to turn that off, we need to use the appropriate `scale` kwarg in `alt.Y()`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "data": { "values": [ { "V (uV)": 4.79, "t (ms)": 703.96 }, { "V (uV)": -0.63, "t (ms)": 704 }, { "V (uV)": 5.83, "t (ms)": 704.04 }, { "V (uV)": 0.31, "t (ms)": 704.08 }, { "V (uV)": -4.58, "t (ms)": 704.12 }, { "V (uV)": 21.67, "t (ms)": 704.16 }, { "V (uV)": 8.13, "t (ms)": 704.2 }, { "V (uV)": 5.94, "t (ms)": 704.24 }, { "V (uV)": 14.79, "t (ms)": 704.28 }, { "V (uV)": 2.71, "t (ms)": 704.32 }, { "V (uV)": 0.94, "t (ms)": 704.36 }, { "V (uV)": 17.4, "t (ms)": 704.4 }, { "V (uV)": 11.77, "t (ms)": 704.44 }, { "V (uV)": -2.6, "t (ms)": 704.48 }, { "V (uV)": 6.04, "t (ms)": 704.52 }, { "V (uV)": -3.44, "t (ms)": 704.56 }, { "V (uV)": -6.04, "t (ms)": 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(df\n", " ).mark_line(\n", " ).encode(\n", " y=alt.Y('t (ms):Q', scale=alt.Scale(zero=False)),\n", " x=alt.X('V (uV):Q', title='V (µV)'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot is clearly messed up. That is because Altair connected the lines in order of voltage. To prevent this, we can use the `order` kwarg of `encode()` to make sure the points are connected in the proper order." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "data": { "values": [ { "V (uV)": 4.79, "t (ms)": 703.96 }, { "V (uV)": -0.63, "t (ms)": 704 }, { "V (uV)": 5.83, "t (ms)": 704.04 }, { "V (uV)": 0.31, "t (ms)": 704.08 }, { "V (uV)": -4.58, "t (ms)": 704.12 }, { "V (uV)": 21.67, "t (ms)": 704.16 }, { "V (uV)": 8.13, "t (ms)": 704.2 }, { "V (uV)": 5.94, "t (ms)": 704.24 }, { "V (uV)": 14.79, "t (ms)": 704.28 }, { "V (uV)": 2.71, "t (ms)": 704.32 }, { "V (uV)": 0.94, "t (ms)": 704.36 }, { "V (uV)": 17.4, "t (ms)": 704.4 }, { "V (uV)": 11.77, "t (ms)": 704.44 }, { "V (uV)": -2.6, "t (ms)": 704.48 }, { "V (uV)": 6.04, "t (ms)": 704.52 }, { "V (uV)": -3.44, "t (ms)": 704.56 }, { "V (uV)": -6.04, "t (ms)": 704.6 }, { "V (uV)": 10.42, "t (ms)": 704.64 }, { "V (uV)": 19.06, "t (ms)": 704.68 }, { "V (uV)": 7.4, "t (ms)": 704.72 }, { "V (uV)": 9.79, "t (ms)": 704.76 }, { "V (uV)": 20.42, "t (ms)": 704.8 }, { "V (uV)": -0.1, "t (ms)": 704.84 }, { "V (uV)": -7.92, "t (ms)": 704.88 }, { "V (uV)": -7.29, "t (ms)": 704.92 }, { "V (uV)": -11.25, "t (ms)": 704.96 }, { "V (uV)": -12.19, "t (ms)": 705 }, { "V (uV)": 12.6, "t (ms)": 705.04 }, { "V (uV)": -1.98, "t (ms)": 705.08 }, { "V (uV)": -10.31, "t (ms)": 705.12 }, { "V (uV)": 0.1, "t (ms)": 705.16 }, { "V (uV)": -5.31, "t (ms)": 705.2 }, { "V (uV)": -8.02, "t (ms)": 705.24 }, { "V (uV)": 15.52, "t (ms)": 705.28 }, { "V (uV)": 4.17, "t (ms)": 705.32 }, { "V (uV)": -5.31, "t (ms)": 705.36 }, { "V (uV)": 3.65, "t (ms)": 705.4 }, { "V (uV)": -5.52, "t (ms)": 705.44 }, { "V (uV)": -11.25, "t (ms)": 705.48 }, { "V (uV)": 4.06, "t (ms)": 705.52 }, { "V (uV)": 6.25, "t (ms)": 705.56 }, { "V (uV)": -1.25, "t (ms)": 705.6 }, { 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"text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(df\n", " ).mark_line(\n", " ).encode(\n", " y=alt.Y('t (ms):Q', scale=alt.Scale(zero=False)),\n", " x=alt.X('V (uV):Q', title='V (µV)'),\n", " order=alt.Order('t (ms):Q', sort='ascending'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, if I am making line plots, I always use an `order` kwarg in the encoding, even if the default behavior is what I want. It's better to be explicit." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting generated data\n", "You're now a pro at plotting measured data. But sometimes, you want to plot smooth functions. To do this, you can use Numpy and/or Scipy to generate arrays of values of smooth functions that you can then make into a data frame and plot with Altair.\n", "\n", "We will plot the [Airy disk](https://en.wikipedia.org/wiki/Airy_disk), which we encounter in biology when doing microscopy as the diffraction pattern of light passing through a pinhole. Here is a picture of the diffraction pattern from a laser (with the main peak overexposed).\n", "\n", "![airy_disk.png](airy_disk.png)\n", "\n", "The equation for the radial light intensity of an Airy disk is\n", "\n", "\\begin{align}\n", "\\frac{I(x)}{I_0} = 4 \\left(\\frac{J_1(x)}{x}\\right)^2,\n", "\\end{align}\n", "\n", "where $I_0$ is the maximum intensity (the intensity at the center of the image) and $x$ is the radial distance from the center. Here, $J_1(x)$ is the first order Bessel function of the first kind. Yeesh. How do we plot *that*?\n", "\n", "Fortunately, SciPy has lots of special functions available. Specifically, `scipy.special.j1()` computes exactly what we are after! We pass in a NumPy array that has the values of $x$ we want to plot and then compute the $y$-values using the expression for the normalized intensity.\n", "\n", "To plot a smooth curve, we use the `np.linspace()` function with lots of points. We then connect the points with straight lines, which to the eye look like a smooth curve. Let's try it. We'll use 400 points, which I find is a good rule of thumb for not-too-oscillating functions." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The x-values we want\n", "x = np.linspace(-15, 15, 400)\n", "\n", "# The normalized intensity\n", "norm_I = 4 * (scipy.special.j1(x) / x)**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the values we want to plot, we construct a Pandas `DataFrame`. We can do this by instantiating the `pd.DataFrame` class and passing in a dictionary containing the column names and data we generated." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x norm_I\n", "0 -15.000000 0.000748\n", "1 -14.924812 0.000767\n", "2 -14.849624 0.000777\n", "3 -14.774436 0.000779\n", "4 -14.699248 0.000772" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_airy = pd.DataFrame(data={'x': x, 'norm_I': norm_I})\n", "\n", "df_airy.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now use this to create an Altair `Chart`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "data": { "values": [ { "norm_I": 0.000747869629432488, "x": -15 }, { "norm_I": 0.0007666845741053045, "x": -14.924812030075188 }, { "norm_I": 0.0007772102463126346, "x": -14.849624060150376 }, { "norm_I": 0.0007790840608934203, "x": -14.774436090225564 }, { "norm_I": 0.0007721312263746704, 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(df_airy,\n", " height=150\n", " ).mark_line(\n", " ).encode(\n", " x='x:Q',\n", " y=alt.Y('norm_I:Q', title='I(x)/I₀'),\n", " order='x:Q')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, `ax.plot()` plots lines. We could also plot dots (which doesn't make sense here, but we'll show it just to see). 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(df_airy,\n", " height=150\n", " ).mark_point(\n", " size=5,\n", " filled=True,\n", " opacity=1\n", " ).encode(\n", " x='x:Q',\n", " y=alt.Y('norm_I:Q', title='I(x)/I₀'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is one detail I swept under the rug here. What happens if we compute the function for $x = 0$?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Justin/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in double_scalars\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "text/plain": [ "nan" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4 * (scipy.special.j1(0) / 0)**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get a `RuntimeWarning` because we divided by zero. We know that\n", "\n", "\\begin{align}\n", "\\lim_{x\\to 0} \\frac{J_1(x)}{x} = \\frac{1}{2},\n", "\\end{align}\n", "\n", "so we could write a new function that checks if $x = 0$ and returns the appropriate limit for $x = 0$. In the `x` array I constructed for the plot, we hopped over zero, so it was never evaluated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting ECDFs\n", "\n", "We have mentioned them many times so far in the bootcamp, and now it is time to plot ECDFs. Remember, an ECDF evaluated at x for a set of measurements is defined as\n", "\n", "> ECDF(*x*) = fraction of measurements ≤ *x*.\n", "\n", "If we have a `DataFrame`, we can compute the y-values of the ECDF to go with the quantitative column of interest. We worked out a function to compute the ECDF values in a previous lesson. I included the function in the `bootcamp_utils`. As a reminder, here is its full specification." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mbootcamp_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mecdf_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mecdf_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"Give y-values of an ECDF for an unsorted column in a data frame.\u001b[0m\n", "\u001b[0;34m \u001b[0m\n", "\u001b[0;34m Parameters\u001b[0m\n", "\u001b[0;34m ----------\u001b[0m\n", "\u001b[0;34m data : Pandas Series\u001b[0m\n", "\u001b[0;34m Series (or column of a DataFrame) from which to generate ECDF\u001b[0m\n", "\u001b[0;34m values\u001b[0m\n", "\u001b[0;34m\u001b[0m\n", "\u001b[0;34m Returns\u001b[0m\n", "\u001b[0;34m -------\u001b[0m\n", "\u001b[0;34m output : Pandas Series\u001b[0m\n", "\u001b[0;34m Corresponding y-values for an ECDF when plotted with dots.\u001b[0m\n", "\u001b[0;34m\u001b[0m\n", "\u001b[0;34m Notes\u001b[0m\n", "\u001b[0;34m -----\u001b[0m\n", "\u001b[0;34m .. This only works for plotting an ECDF with points, not for formal\u001b[0m\n", "\u001b[0;34m ECDFs\u001b[0m\n", "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'first'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFile:\u001b[0m ~/Dropbox/git/bootcamp/modules/bootcamp_utils/bootcamp_utils/viz.py\n", "\u001b[0;31mType:\u001b[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bootcamp_utils.ecdf_y??" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use this to make a plot of ECDFs of facial matching data for insomniacs and non-insomniacs. We'll start by loading the data and including the insomnia column." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Load in the data set\n", "df = pd.read_csv('data/gfmt_sleep.csv', comment='#')\n", "\n", "# Compute insomnia\n", "df['insomnia'] = df['sci'] <= 16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can group by the `insomnia` column and apply the transform defined in the `ecdf_y()` function." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Compute y-values for the ECDF for percent correct for each gender\n", "grouped = df.groupby('insomnia')\n", "df['ecdf_y grouped by insomnia'] = grouped['percent correct'].transform(bootcamp_utils.ecdf_y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, we're ready to plot the ECDF." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(df,\n", " ).mark_point(\n", " ).encode(\n", " x=alt.X('percent correct:Q', scale=alt.Scale(zero=False)),\n", " y=alt.Y('ecdf_y grouped by insomnia:Q', title='ECDF'),\n", " color=alt.Color('insomnia:N'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look for example at the data point at 50% correct. That point in the ECDF denotes that about 8% of observations from insomniacs had 50% or less correct responses in the test.\n", "\n", "Plotting the ECDF explicitly shows all data points. However, this is strictly speaking not a plot of an ECDF. Remember the y-value of the an ECDF at point *x* is the fraction of all observations less than or equal to *x*. This means that the support of the ECDF is on the entire real number line. In otherwords, the ECDF does have a value at 52% correct. Specifically, ECDF(52) = 0.08. So, formally, the ECDF should be plotted as a line. This requires us to fill in more points than are available in the `DataFrame`, which means we can do some Numpy wrangling. I wrote a function in the `bootcamp_utils` module to do this. Below is the function, and you can look carefully at how I sliced the Numpy arrays to generate a clear staircase-like ECDF." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mbootcamp_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mecdf_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_min\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mecdf_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_min\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"Get x, y, values of an ECDF for plotting.\u001b[0m\n", "\u001b[0;34m\u001b[0m\n", "\u001b[0;34m Parameters\u001b[0m\n", "\u001b[0;34m ----------\u001b[0m\n", "\u001b[0;34m data : ndarray\u001b[0m\n", "\u001b[0;34m One dimensional Numpay array with data.\u001b[0m\n", "\u001b[0;34m formal : bool, default False\u001b[0m\n", "\u001b[0;34m If True, generate x and y values for formal ECDF (staircase). If\u001b[0m\n", "\u001b[0;34m False, generate x and y values for ECDF as dots.\u001b[0m\n", "\u001b[0;34m x_min : float, 'infer', or None\u001b[0m\n", "\u001b[0;34m Minimum value of x to plot. If 'infer', use a 5% buffer. Ignored\u001b[0m\n", "\u001b[0;34m if `formal` is False.\u001b[0m\n", "\u001b[0;34m x_max : float, 'infer', or None\u001b[0m\n", "\u001b[0;34m Maximum value of x to plot. If 'infer', use a 5% buffer. Ignored\u001b[0m\n", "\u001b[0;34m if `formal` is False.\u001b[0m\n", "\u001b[0;34m\u001b[0m\n", "\u001b[0;34m Returns\u001b[0m\n", "\u001b[0;34m -------\u001b[0m\n", "\u001b[0;34m x : ndarray\u001b[0m\n", "\u001b[0;34m x-values for plot\u001b[0m\n", "\u001b[0;34m y : ndarray\u001b[0m\n", "\u001b[0;34m y-values for plot\u001b[0m\n", "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m 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at y=0\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx_min\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx_min\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'infer'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mx_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m 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\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mx_max\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x_max < x.max().'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mx_formal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_formal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_max\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0my_formal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_formal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx_formal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_formal\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFile:\u001b[0m ~/Dropbox/git/bootcamp/modules/bootcamp_utils/bootcamp_utils/viz.py\n", "\u001b[0;31mType:\u001b[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bootcamp_utils.ecdf_vals??" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also included in the module is a handy function to give you a `DataFrame` that you can use to plot formal ECDFs (or ECDFs with dots if you like) using Altair." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "formal = alt.Chart(df_ecdf\n", " ).mark_line(\n", " ).encode(\n", " x='percent correct:Q',\n", " y='ECDF:Q',\n", " color='insomnia:N',\n", " order='percent correct:Q')\n", "\n", "formal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are a couple of formal ECDFs. To see how they relate to ECDFs with dots, we can overlay the two." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "layer": [ { "data": { "values": [ { "ECDF": 0, "insomnia": false, "percent correct": 37 }, { "ECDF": 0, "insomnia": false, "percent correct": 40 }, { "ECDF": 0, "insomnia": false, "percent correct": 40 }, { "ECDF": 0.012987012987012988, "insomnia": false, "percent correct": 40 }, { "ECDF": 0.012987012987012988, "insomnia": false, "percent correct": 55 }, { "ECDF": 0.025974025974025976, "insomnia": false, "percent correct": 55 }, { "ECDF": 0.025974025974025976, "insomnia": false, "percent correct": 57.5 }, { "ECDF": 0.03896103896103896, "insomnia": false, "percent correct": 57.5 }, { "ECDF": 0.03896103896103896, "insomnia": false, "percent correct": 60 }, { "ECDF": 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dots = alt.Chart(df,\n", " ).mark_point(\n", " ).encode(\n", " x=alt.X('percent correct:Q', scale=alt.Scale(zero=False)),\n", " y=alt.Y('ecdf_y grouped by insomnia:Q', title='ECDF'),\n", " color=alt.Color('insomnia:N', legend=None))\n", "\n", "formal + dots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dots are the concave corners of steps in the staircase. They give the same amount of information as the steps. For intermediate numbers of points in a data set, I prefer dots. For large or very small data sets, I prefer formal ECDFs." ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }