{
“cells”: [
{

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“McsPyDataTools Tutorial<a id=’Top’></a>n”, “=======================n”, “n”, “This tutorial gives an overview over the file structure of MCS HDF5 files and the usage of the McsPyDataTools toolbox to interact with these files.n”, “n”, “- <a href=’#D and I’>Downloading and installing</a>n”, “—————————————————n”, “- <a href=’#Mcs-HDF5’>Structure of the Mcs-HDF5 file</a>n”, “——————————————————–n”, “- <a href=’#McsData Module’>McsData Classes and Inheritance</a>n”, “——————————————————————————————-n”, ” - ### <a href=’#RD’>RawData</a> n”, ” - ### <a href=’#R’>Recording</a> n”, ” - ### <a href=’#S’>Stream</a>n”, ” - ### <a href=’#I’>Info</a> n”, ” n”, “- <a href=’#Accessing your Data with McsData’>Accessing your Data with McsData</a>n”, “———————————————————————————-n”, ” - ### <a href=’#Req’>Requirements</a>n”, ” - ### <a href=’#AS’>AnalogStream</a>n”, ” - ### <a href=’#FS’>FrameStream</a>n”, ” - ### <a href=’#ES’>EventStream</a>n”, ” - ### <a href=’#SS’>SegmentStream</a>n”, ” - #### <a href=’#SC’>Subtype: Cutouts</a>n”, ” - #### <a href=’#SA’>Subtype: Averages</a>n”, ” - ### <a href=’#TS’>TimestampStream</a>n”, ” - ### <a href=’#I2’>Info</a>n”, “n”, “n”, “Downloading and installing<a id=’D and I’></a>n”, “———————————————-n”, “n”, “Open a console or terminal andn”, “n”, “n”, “### - With pip or setuptoolsn”, “n”, “type:n”, “n”, ” pip install McsPyDataToolsn”, ” n”, “Or if you have setuptools installed type:n”, “n”, ” easy_install McsPyDataToolsn”, ” n”, “n”, “If this doesn’t yield the expected result, download the most current .zip file from the [Multi Channel DataManager](https://www.multichannelsystems.com/software/multi-channel-datamanager) page.n”, “n”, “From here there are 4 possible ways to get the module working (replace {VERSION} with the most current version of the toolbox, e.g. 0.4.0):n”, “n”, “n”, “### - Manually(while packed)n”, ” n”, “Go to your Downloads folder and run:n”, ” n”, ” pip install McsPyDataTools-{VERSION}.zip n”, ” n”, “Or if you have setuptools installed:n”, “n”, ” easy_install McsPyDataTools-{VERSION}.zip n”, ” n”, “n”, “If the methods above fail, unpack the .zip file.n”, “n”, “### - Manually (when unpacked) In”, “n”, “Go to the folder you unpacked the module to and run:n”, ” n”, ” pip install McsPyDataTools-{VERSION}n”, ” n”, “Or if you have setuptools installed:n”, “n”, ” easy_install McsPyDataTools-{VERSION}n”, ” n”, ” n”, “### - Manually (when unpacked) IIn”, “n”, “Go to the folder you unpacked the module to, go to the McsPyDataTools_{VERSION} folder and run the setup.py file from inside the foldern”, “n”, ” python setup.py installn”, ” n”, “If either of the above worked there will be an McsPyDataTools-{VERSION}-py3.6.eggn”, “in the site_package folder as well as an McsPyDataTools.py and a PlotExperimentData.py script in the Scripts folder of your Python installation.n”, ” n”, ” n”, “### - Manually (when unpacked) IIIn”, “n”, “If the other ways fail or you are still unable to import the module into a python script, you can manually place the McsData.py and McsCMOS.py scripts from the McsPyDataTools folder in the \site-packages folder of your Python installation.n”, “n”, “Note that the folder containing your Python installation might be hidden.n”, “n”, “This last option will only make the classes available needed to analyze HDF5 files. Any other scripts, like the DataStreamInfo.py or the McsDataTools.py script, which should get installed to the /Scripts folder of your python installation, are best copied into a seperate folder.n”, “n”, “### Datan”, “n”, “This notebook relies on some files which hold the data for the examples. These files are quite large and can be found in the subfolder TestData of this archive. A larger set of test data can be downloaded from https://www.multichannelsystems.com/software/multi-channel-datamanagern”, “n”, “<a href=’#Top’>Back to index</a>n”, “n”, “Structure of the Mcs-HDF5 file<a id=’Mcs-HDF5’></a>n”, “—————————————————n”, “n”, “With the included DataStreamInfo.py script, a first look can be taken at the contents of the HDF5 file.n”, “n”, “The information about the data within the file can be viewed by calling the DataStreamInfo.py script from the console and handing over the exact file-path with the argument for directory and filepath: -fn”, “n”, ” X:...\python DataStreamInfo.py -f “X:\Data\Experiment_231\2014-07-09T10-17-35W8 Standard all 500 Hz.h5” n”, ” n”, “If the desired file is in the same folder as DataStreamInfo.py, you might want to consider copying this script to your datafolder, –f + “Filename” can be used:n”, “n”, ” X:...\python DataStreamInfo.py –f “2014-07-09T10-17-35W8 Standard all 500 Hz.h5”n”, ” n”, “A table like this will appear:n”, “n”, ” 2014-07-09T10-17-35W8 Standard all 500 Hz.h5n”, “n”, ” Date Program Versionn”, ” ——————- ————————– ———n”, ” 2014-07-09 10:17:35 Multi Channel Experimenter 0.9.8.2n”, ” n”, ” Type Stream # chn”, ” ——— ————————————— ——n”, ” Analog Filter (1) Filter Data 8n”, ” Analog Data Acquisition (1) Electrode Raw Data 8n”, ” Analog Data Acquisition (1) Digital Data 1n”, ” Event Digital Events 1n”, ” Segment Spike Detector (1) Spike Datan”, ” TimeStamp Spike Detector (1) Spike Timestampsn”, ” n”, “It holds information about the Date of the recording, the Program which was used as well as its Version. Also included is a list of Streams and additional information concerning these. Streams can be seen as containers of information of a certain type.n”, “n”, “<a href=’#Top’>Back to index</a>n”, “n”, “McsData Classes and Inheritance <a id=’McsData Module’></a>n”, “—————————————————————————————n”, “n”, “This is a graphical representation of the classes and their content which may be found in an HDF5 file produced by MCS software.n”, “n”, “<img src=”./Hierarchy_short.png”>n”, “n”, “Additional information about the member methods of each class can be found in McsData.py or the module description file.n”, “n”, “We also highly recommend the use of the HDF Groups HDFView software to help visualize and understand the structure of HDF5 files. This can make accessing the data MUCH easier.n”, “n”, “<a href=’#Top’>Back to index</a>n”, “n”, “### RawData <a id=’RD’></a>n”, “n”, “As the docstrings of the class already imply, this class is designed to hold the information of a complete MCS raw data file. n”, “n”, “Upon initialization with the path to your raw datan”, “n”, “`python\n", "    rawdata = RawData('path to your raw data')\n", "`n”, “n”, “member methods of this class will check if the provided file meets the version requirements to be further processed. This is necessary, as not only the way how MCS software handles the HDF5 formatted files may change but the file format itself can undergo changes.n”, “n”, “`python\n", "    self.__validate_mcs_hdf5_version()\n", "`n”, “n”, “Afterwards all information about the data stored in the file is retrieved.n”, “n”, “`python\n", "    self.__get_session_info()\n", "`n”, “n”, “When needed all recordings from the raw data file are read byn”, “n”, “`python\n", "    self.__read_recordings()\n", "`n”, “n”, “This generates a dictionary with the number of the recordings as keys, Recording\_**0**, Recording\_**1**, etc. and the values as members of the Recording class with the corresponding data. This will be important when we discuss the possibility of iterating over all datasets within one group.n”, “n”, “<a href=’#Top’>Back to index</a>n”, “n”, “### Recording <a id=’R’></a>n”, “n”, “The Recording class can be seen as a container for all the data gathered in one recording.n”, “n”, “`python\n", "    class RawData(object):\n", "    \n", "        ...\n", "        \n", "        self.__recordings[int(recording_name[1])] = Recording(value)\n", "`n”, “n”, “Upon initialization the values extracted by the RawData class get assigned to it.n”, “n”, “`python\n", "    class Recording(object):\n", "    \n", "        ...\n", "        \n", "        self.__recording_grp = recording_grp  # recording_grp = value\n", "`n”, “Later on these can be further decomposed by the member methods of this class into the different subtypes/children of the Stream class.n”, “n”, “`python\n", "    self.__read_analog_streams()\n", "    self.__read_frame_streams()\n", "    self.__read_event_streams()\n", "    self.__read_segment_streams()\n", "    self.__read_timestamp_streams()\n", "` n”, ” n”, “The data of the streams gets assigned in a similar fashion as seen before with the recordingsn”, “n”, “`python\n", "    class Recording(object):\n", "\n", "        ...\n", "        \n", "        if 'AnalogStream' in self.__recording_grp:\n", "            \n", "            ...\n", "        \n", "            self.__analog_streams[int(stream_name[1])] = AnalogStream(value)\n", "        \n", "    \n", "    \n", "    class AnalogStream(Stream):\n", "    \n", "        ...\n", "        \n", "        Stream.__init__(self, stream_grp, \"AnalogStreamInfoVersion\")  # stream_grp = value\n", "`n”, “n”, “Due to this internal structure of classes and subclasses, being only created when addressed, only small portions of the data ever get loaded at any time, speeding up the access and the computation of those values.n”, “n”, “<a href=’#Top’>Back to index</a>n”, “n”, “### Stream <a id=’S’></a>n”, “n”, “The Stream class is the base class from which all stream types inherit. All describing metadata of the stream is read here.n”, “n”, “Currently the following types exist:n”, ” - AnalogStreamn”, ” - FrameStreamn”, ” - EventStreamn”, ” - SegmentStreamn”, ” - TimeStampStreamn”, ” n”, “These streams can be further split up into single entities of the corresponding type. So FrameStream can contain several FrameEntities. These Entities finally hold the data which, once addressed, can be viewed, manipulated and/or visualized.n”, “n”, “Additional information about the classes can be found in the html documentation.n”, “n”, “<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Info <a id=’I’></a>n”, “n”, “In addition to the Stream classes, there is the Info class.n”, “This is the parent class of all Info child classes that exist for the different Stream types. Info that gets stored can be timerange of ticks, units of readings, experiment specific information about dilutions, sensor id, filtersettings, etc..n”, “n”, “<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## Accessing your Data with McsData<a id=’Accessing your Data with McsData’></a>n”, “n”, “Now that the mechanism of reading data from an HDF5 file with the classes included in the McsData module is clear we can walk through some quick and easy examples of how to access and visualize your data.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### Requirements <a id=’Req’></a>n”, “n”, “First some modules need to be imported.”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “metadata”: {}, “outputs”: [], “source”: [

“# These are the imports of the McsData modulen”, “import sys, importlib, osn”, “import McsPy.McsDatan”, “import McsPy.McsCMOSn”, “from McsPy import ureg, Q_n”, “n”, “# matplotlib.pyplot will be used in these examples to generate the plots visualizing the datan”, “import matplotlib.pyplot as pltn”, “from matplotlib.figure import Figuren”, “from matplotlib.widgets import Slidern”, “# These adjustments only need to be made so that the plot gets displayed inside the notebookn”, “%matplotlib inlinen”, “# %config InlineBackend.figure_formats = {‘png’, ‘retina’}n”, “n”, “# numpy is numpy …n”, “import numpy as npn”, “n”, “# bokeh adds more interactivity to the plots within notebooks. Adds toolbar at the top-right corner of the plot.n”, “# Allows zooming, panning and saving of the plotn”, “import bokeh.ion”, “import bokeh.plottingn”, “from bokeh.palettes import Spectral11”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Sometimes running Python applications in the background can interfere with the functionalities of this notebook. To make sure that all plots are created correctly you are best advised to exit any other Python related processes.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Then, we need to define where the test data is located. This needs to be adjusted to your local setup! The McsPyDataTools toolbox includes a set of small test files in its tests/TestData folder. An archive with larger test files can be downloaded from the [Multi Channel DataManager](https://www.multichannelsystems.com/software/multi-channel-datamanager) page.”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “metadata”: {}, “outputs”: [], “source”: [

“test_data_folder = r’..\McsPyDataTools\McsPy\tests\TestData’ # adjust this!”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### AnalogStream<a id=’AS’></a>n”, “n”, “Next we need to access the raw data by initializing an instance of the RawData class from the McsData module by handing over the path to the file.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“To check if we got access to the file we can look at its contents by printing the info that got extracted when the RawData object was initialized. This is just for demonstrational purposes and does not need to be made every time data is accessed.”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “metadata”: {}, “outputs”: [], “source”: [

“channel_raw_data = McsPy.McsData.RawData(os.path.join(test_data_folder, ‘2014-07-09T10-17-35W8 Standard all 500 Hz.h5’))”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“n”, “2014-07-09 10:17:35.172096n”, “Mittwoch, 9. Juli 2014n”, “635404978551720981n”, “700b3ec2-d406-4943-bcef-79d73f0ac4d3n”, “Linear8n”, “n”, “Linear8n”, “Multi Channel Experimentern”, “0.9.8.2n”

]

}

], “source”: [

“print(channel_raw_data.comment)n”, “print(channel_raw_data.date)n”, “print(channel_raw_data.clr_date)n”, “print(channel_raw_data.date_in_clr_ticks)n”, “print(channel_raw_data.file_guid)n”, “print(channel_raw_data.mea_name)n”, “print(channel_raw_data.mea_sn)n”, “print(channel_raw_data.mea_layout)n”, “print(channel_raw_data.program_name)n”, “print(channel_raw_data.program_version)”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Recording_0 <HDF5 group “/Data/Recording_0” (4 members)>n”, “{0: <McsPy.McsData.Recording object at 0x0000016BFB38DAC8>}n”

]

}

], “source”: [

“print(channel_raw_data.recordings)”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Stream_0 <HDF5 group “/Data/Recording_0/AnalogStream/Stream_0” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (8, 9850), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (8,), type “|V100”>n”, “Stream_1 <HDF5 group “/Data/Recording_0/AnalogStream/Stream_1” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (8, 9800), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (8,), type “|V100”>n”, “Stream_2 <HDF5 group “/Data/Recording_0/AnalogStream/Stream_2” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (1, 9800), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (1,), type “|V100”>n”, “{0: <McsPy.McsData.AnalogStream object at 0x0000016BFB38DF28>, 1: <McsPy.McsData.AnalogStream object at 0x0000016BFB38DF98>, 2: <McsPy.McsData.AnalogStream object at 0x0000016BFB39F048>}n”

]

}

], “source”: [

“print(channel_raw_data.recordings[0].analog_streams)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Additionally, the indices or IDs of the included data structures can be addressed by calling .keys() on the HDF5 groups. This is due to the fact that inside of McsData, upon initialization of the different data structure types, dictionaries are created with IDs as keys and values of the data as values.n”, “n”, “This will become more important later in this tutorial when the procedure of iterating over all data of one stream is displayed.”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“dict_keys([0])n”

]

}

], “source”: [

“print(channel_raw_data.recordings.keys())”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“So we see that there is one Recording within the raw data with index 0,”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“dict_keys([0, 1, 2])n”

]

}

], “source”: [

“print(channel_raw_data.recordings[0].analog_streams.keys())”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“and it includes 3 AnalogStreams at index 0,1 and 2n”, “n”, ” (u’Stream_0’, <HDF5 group “/Data/Recording_0/AnalogStream/Stream_0” (3 members)>)n”, ” (u’Stream_1’, <HDF5 group “/Data/Recording_0/AnalogStream/Stream_1” (3 members)>)n”, ” (u’Stream_2’, <HDF5 group “/Data/Recording_0/AnalogStream/Stream_2” (3 members)>)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“From looking at the file with DataStreamInfo.py we know what these streams are.n”, “n”, ” Type Stream # chn”, ” ——— ————————————— ——n”, ” Analog Filter (1) Filter Data 8 <—– Index: 0n”, ” Analog Data Acquisition (1) Electrode Raw Data 8n”, ” Analog Data Acquisition (1) Digital Data 1n”, “n”, “n”, “So the first of the three streams is addressed like:n”

]

}, {

“cell_type”: “code”, “execution_count”: 9, “metadata”: {}, “outputs”: [], “source”: [

“analog_stream_0 = channel_raw_data.recordings[0].analog_streams[0]”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The data of the stream can be found under .channel_data. It is only now that the actual values from the data get accessed. Before this step, we only navigated through pointers of sorts containing information leading to the data. This behavior makes working with HDF5 so efficient.”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“<HDF5 dataset “ChannelData”: shape (8, 9850), type “<i4”>n”

]

}

], “source”: [

“analog_stream_0_data = analog_stream_0.channel_datan”, “n”, “print(analog_stream_0_data)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“By rearranging the dimensions of the data-array with the numpy function transpose() its dimensions are more suitable for plotting. “

]

}, {

“cell_type”: “code”, “execution_count”: 11, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Old shape: (8, 9850)n”, “New shape: (9850, 8)n”, “n”, “[[-2 -1 0 … -5 0 2]n”, ” [ 3 -1 -4 … 2 5 -2]n”, ” [-2 5 2 … -1 -4 0]n”, ” …n”, ” [ 7 0 -2 … 0 4 6]n”, ” [-5 -2 -2 … 0 -3 -8]n”, ” [ 0 3 7 … 0 8 2]]n”

]

}

], “source”: [

“np_analog_stream_0_data = np.transpose(analog_stream_0_data)n”, “n”, “print(“Old shape:”, analog_stream_0_data.shape)n”, “print(“New shape:”, np_analog_stream_0_data.shape)n”, “print()n”, “print(np_analog_stream_0_data)”

]

}, {

“cell_type”: “code”, “execution_count”: 12, “metadata”: {}, “outputs”: [

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plt.plot(np_analog_stream_0_data)n”, “n”, “plt.show()”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“A refined plots with added axis lables and title might look like this”

]

}, {

“cell_type”: “code”, “execution_count”: 13, “metadata”: {}, “outputs”: [

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 864x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plt.figure(figsize=(12,6))n”, “plt.plot(np_analog_stream_0_data)n”, “plt.title(‘Signal for Wireless (Simulation) / Raw ADC-Values (%s)’ % analog_stream_0.label)n”, “plt.xlabel(‘Sample Index’)n”, “plt.ylabel(‘ADC Value’)n”, “plt.grid()n”, “n”, “plt.show()”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“If you want to look at certain portions of the data this can be achieved by specifying a range when accessing it.n”, “n”, “To specify a range it helps to know the shape of your data. “

]

}, {

“cell_type”: “code”, “execution_count”: 14, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“(9850, 8)n”

]

}

], “source”: [

“np_analog_stream_0_data = np.transpose(channel_raw_data.recordings[0].analog_streams[0].channel_data)n”, “n”, “print(np_analog_stream_0_data.shape)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“So it seems that this data-array has 9850 rows and 8 columns. n”, “n”, “Let’s look at rows 4400 to 4800 in columns 4 to 7. Notice that in the HDF5 file rows and colums are swapped. As python doesn’t include the last item in range we have to add 1 to both ranges.”

]

}, {

“cell_type”: “code”, “execution_count”: 15, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[[ 0 5 -2 5]n”, ” [ 0 0 1 1]n”, ” [-6 -2 0 -2]n”, ” …n”, ” [-2 0 -9 0]n”, ” [ 0 -1 5 4]n”, ” [-5 5 -2 -4]]n”

]

}

], “source”: [

“np_data_range = np_analog_stream_0_data[4500:4801, 4:8] n”, “n”, “print(np_data_range)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“And then just plot!”

]

}, {

“cell_type”: “code”, “execution_count”: 16, “metadata”: {}, “outputs”: [

{
“data”: {
“text/html”: [
“n”, ” <div class=”bk-root”>n”, ” <a href=”https://bokeh.org” target=”_blank” class=”bk-logo bk-logo-small bk-logo-notebook”></a>n”, ” <span id=”1001”>Loading BokehJS …</span>n”, ” </div>”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
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If loading BokehJS from CDN, this \n”+n”, ” “may be due to a slow or bad network connection. Possible fixes:\n”+n”, ” “</p>\n”+n”, ” “<ul>\n”+n”, ” “<li>re-rerun output_notebook() to attempt to load from CDN again, or</li>\n”+n”, ” “<li>use INLINE resources instead, as so:</li>\n”+n”, ” “</ul>\n”+n”, ” “<code>\n”+n”, ” “from bokeh.resources import INLINE\n”+n”, ” “output_notebook(resources=INLINE)\n”+n”, ” “</code>\n”+n”, ” “</div>”}};n”, “n”, ” function display_loaded() {n”, ” var el = document.getElementById(“1001”);n”, ” if (el != null) {n”, ” el.textContent = “BokehJS is loading…”;n”, ” }n”, ” if (root.Bokeh !== undefined) {n”, ” if (el != null) {n”, ” el.textContent = “BokehJS ” + root.Bokeh.version + ” successfully loaded.”;n”, ” }n”, ” } else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(display_loaded, 100)n”, ” }n”, ” }n”, “n”, “n”, ” function run_callbacks() {n”, ” try {n”, ” root._bokeh_onload_callbacks.forEach(function(callback) {n”, ” if (callback != null)n”, ” callback();n”, ” });n”, ” } finally {n”, ” delete root._bokeh_onload_callbacksn”, ” }n”, ” console.debug(“Bokeh: all callbacks have finished”);n”, ” }n”, “n”, ” function load_libs(css_urls, js_urls, callback) {n”, ” if (css_urls == null) css_urls = [];n”, ” if (js_urls == null) js_urls = [];n”, “n”, ” root._bokeh_onload_callbacks.push(callback);n”, ” if (root._bokeh_is_loading > 0) {n”, ” console.debug(“Bokeh: BokehJS is being loaded, scheduling callback at”, now());n”, ” return null;n”, ” }n”, ” if (js_urls == null || js_urls.length === 0) {n”, ” run_callbacks();n”, ” return null;n”, ” }n”, ” console.debug(“Bokeh: BokehJS not loaded, scheduling load and callback at”, now());n”, ” root._bokeh_is_loading = css_urls.length + js_urls.length;n”, “n”, ” function on_load() {n”, ” root._bokeh_is_loading–;n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: all BokehJS libraries/stylesheets loaded”);n”, ” run_callbacks()n”, ” }n”, ” }n”, “n”, ” function on_error() {n”, ” console.error(“failed to load ” + url);n”, ” }n”, “n”, ” for (var i = 0; i < css_urls.length; i++) {n”, ” var url = css_urls[i];n”, ” const element = document.createElement(“link”);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.rel = “stylesheet”;n”, ” element.type = “text/css”;n”, ” element.href = url;n”, ” console.debug(“Bokeh: injecting link tag for BokehJS stylesheet: “, url);n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” for (var i = 0; i < js_urls.length; i++) {n”, ” var url = js_urls[i];n”, ” var element = document.createElement(‘script’);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.async = false;n”, ” element.src = url;n”, ” console.debug(“Bokeh: injecting script tag for BokehJS library: “, url);n”, ” document.head.appendChild(element);n”, ” }n”, ” };var element = document.getElementById(“1001”);n”, ” if (element == null) {n”, ” console.error(“Bokeh: ERROR: autoload.js configured with elementid ‘1001’ but no matching script tag was found. “)n”, ” return false;n”, ” }n”, “n”, ” function inject_raw_css(css) {n”, ” const element = document.createElement(“style”);n”, ” element.appendChild(document.createTextNode(css));n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” n”, ” var js_urls = [”https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js”];n”, ” var css_urls = [];n”, ” n”, “n”, ” var inline_js = [n”, ” function(Bokeh) {n”, ” Bokeh.set_log_level(“info”);n”, ” },n”, ” function(Bokeh) {n”, ” n”, ” n”, ” }n”, ” ];n”, “n”, ” function run_inline_js() {n”, ” n”, ” if (root.Bokeh !== undefined || force === true) {n”, ” n”, ” for (var i = 0; i < inline_js.length; i++) {n”, ” inline_js[i].call(root, root.Bokeh);n”, ” }n”, ” if (force === true) {n”, ” display_loaded();n”, ” }} else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(run_inline_js, 100);n”, ” } else if (!root._bokeh_failed_load) {n”, ” console.log(“Bokeh: BokehJS failed to load within specified timeout.”);n”, ” root._bokeh_failed_load = true;n”, ” } else if (force !== true) {n”, ” var cell = $(document.getElementById(“1001”)).parents(‘.cell’).data().cell;n”, ” cell.output_area.append_execute_result(NB_LOAD_WARNING)n”, ” }n”, “n”, ” }n”, “n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: BokehJS loaded, going straight to plotting”);n”, ” run_inline_js();n”, ” } else {n”, ” load_libs(css_urls, js_urls, function() {n”, ” console.debug(“Bokeh: BokehJS plotting callback run at”, now());n”, ” run_inline_js();n”, ” });n”, ” }n”, “}(window));”

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“bokeh.io.output_notebook() # see comment for bokeh module in “Requirements” sectionn”, “bfig = bokeh.plotting.figure(plot_width=900, plot_height=400, title=’Signal for Wireless (Simulation) / Raw ADC-Values (%s)’ % analog_stream_0.label)n”, “bfig.multi_line(n”, ” xs = [list(range(np_data_range.shape[0]))] * np_data_range.shape[1],n”, ” ys = [np_data_range[:, col] for col in range(np_data_range.shape[1])],n”, ” line_color = Spectral11[0:np_data_range.shape[1]],n”, ” alpha = 0.8n”, “)n”, “#bfig.line(list(range(np_data_range.shape[0])),np_data_range[:,0], alpha=0.5)n”, “bfig.xaxis.axis_label = ‘Sample Index’n”, “bfig.yaxis.axis_label = ‘ADC Value’n”, “bfig.ygrid.minor_grid_line_color = ‘navy’n”, “bfig.ygrid.minor_grid_line_alpha = 0.1n”, “bokeh.plotting.show(bfig)”

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“Of course other plot types can be used if desired. “

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“n”, “#### Draw channel with spectogram:n”, “n”, “With the values from the second AnalogStream, n”, “n”, ” Type Stream # chn”, ” ——— ————————————— ——n”, ” Analog Filter (1) Filter Data 8n”, ” Analog Data Acquisition (1) Electrode Raw Data 8 <—— n”, ” Analog Data Acquisition (1) Digital Data 1n”, ” Event Digital Events 1n”, ” Segment Spike Detector (1) Spike Datan”, ” TimeStamp Spike Detector (1) Spike Timestampsn”, “n”, “With .get_channel_in_range(channel_id, index_start, index_end) and .get_channel_sample_timestamps(channel_id, index_start, index_end) we can define a range of data and timestamps, from index_start to index_end for a specific channel with channel_id that we want to analyze/plot.n”, “n”, “However, .get_channel_sample_timestamps(channel_id, index_start, index_end) rather than grabbing an existing data set, calculates timestamps from the Tick value from the InfoChannel structure of the Stream and the provided range. Also using the functions above the data internally is rearranged so no need to use any additional numpy functions here. n”, “n”, “The channel_IDs can be acquired by calling .keys() on the channel_infos of the respective stream.”

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}, {

“cell_type”: “code”, “execution_count”: 17, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“dict_keys([0, 1, 2, 3, 4, 5, 6, 7])n”

]

}

], “source”: [

“channel_ids = channel_raw_data.recordings[0].analog_streams[1].channel_infos.keys()n”, “n”, “print(channel_ids)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“So there are 8 channels within the stream. We take the key at index [0]. Sure this example looks a bit like overkill, but when iterating over multiple channels, this can be a way to go.”

]

}, {

“cell_type”: “code”, “execution_count”: 18, “metadata”: {}, “outputs”: [], “source”: [

“channel_id = list(channel_raw_data.recordings[0].analog_streams[1].channel_infos.keys())[0]”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Additional information can be accessed through .info on .channel_infos[id]

]

}, {

“cell_type”: “code”, “execution_count”: 19, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“{‘ChannelID’: 0, ‘RowIndex’: 0, ‘GroupID’: 0, ‘Label’: ‘E1’, ‘RawDataType’: ‘Int’, ‘Unit’: ‘V’, ‘Exponent’: -9, ‘ADZero’: 0, ‘Tick’: 2000, ‘ConversionFactor’: 381470, ‘HighPassFilterType’: ‘’, ‘HighPassFilterCutOffFrequency’: ‘-1’, ‘HighPassFilterOrder’: -1, ‘LowPassFilterType’: ‘’, ‘LowPassFilterCutOffFrequency’: ‘-1’, ‘LowPassFilterOrder’: -1}n”

]

}

], “source”: [

“print(channel_raw_data.recordings[0].analog_streams[1].channel_infos[0].info)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Back to the plot. Grab the stream and the corresponding timestamps”

]

}, {

“cell_type”: “code”, “execution_count”: 20, “metadata”: {}, “outputs”: [], “source”: [

“stream = channel_raw_data.recordings[0].analog_streams[1]n”, “time = stream.get_channel_sample_timestamps(channel_id, 0, 10000)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Next we recalculate the values saved in the time variable to seconds with the included ureg function from the McsData module and extract the data of the desired channel with its ID and a range(0 to 10000)”

]

}, {

“cell_type”: “code”, “execution_count”: 21, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Signal: (array([-0.00343323, 0.00228882, -0.00419617, …, 0.00991822,n”, ” 0.00724793, 0.00686646]), <Unit(‘volt’)>)n”

]

}

], “source”: [

“# scale time to seconds:n”, “scale_factor_for_second = Q_(1,time[1]).to(ureg.s).magnituden”, “time_in_sec = time[0] * scale_factor_for_secondn”, “n”, “signal = stream.get_channel_in_range(channel_id, 0, 10000)n”, “print(“Signal: “,signal)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“To plot the spectogram of the data we also need to get the sampling frequency. For more information about metainformation of streams and data see the chapter <a href=’#I2’>Info</a>.”

]

}, {

“cell_type”: “code”, “execution_count”: 22, “metadata”: {}, “outputs”: [], “source”: [

“sampling_frequency = stream.channel_infos[channel_id].sampling_frequency.magnitude “

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“And then plot!”

]

}, {

“cell_type”: “code”, “execution_count”: 23, “metadata”: {}, “outputs”: [

{
“data”: {

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”, “text/plain”: [

“<Figure size 1440x864 with 2 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plt.figure(figsize=(20,12))n”, “n”, “# Plot time domainn”, “axtp = plt.subplot(211)n”, “plt.plot(time_in_sec, signal[0])n”, “plt.xlabel(‘Time (%s)’ % ureg.s)n”, “plt.ylabel(‘Voltage (%s)’ % signal[1])n”, “plt.title(‘Sampled signal (%s)’ % stream.label)n”, “n”, “# Plot frequency domainn”, “plt.subplot(212)n”, “plt.specgram(signal[0], NFFT=512, noverlap = 128, Fs = sampling_frequency, cmap = plt.cm.gist_heat, scale_by_freq = False)n”, “plt.xlabel(‘Time (%s)’ % ureg.s)n”, “plt.ylabel(‘Frequency (Hz)’)n”, “plt.show()n”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“n”, “#### Compare multiple streams:n”, “n”, “To compare multiple streams they can also be plotted in one figure. n”

]

}, {

“cell_type”: “code”, “execution_count”: 24, “metadata”: {}, “outputs”: [

{
“data”: {
“text/html”: [
“n”, ” <div class=”bk-root”>n”, ” <a href=”https://bokeh.org” target=”_blank” class=”bk-logo bk-logo-small bk-logo-notebook”></a>n”, ” <span id=”1097”>Loading BokehJS …</span>n”, ” </div>”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
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If loading BokehJS from CDN, this \n”+n”, ” “may be due to a slow or bad network connection. Possible fixes:\n”+n”, ” “</p>\n”+n”, ” “<ul>\n”+n”, ” “<li>re-rerun output_notebook() to attempt to load from CDN again, or</li>\n”+n”, ” “<li>use INLINE resources instead, as so:</li>\n”+n”, ” “</ul>\n”+n”, ” “<code>\n”+n”, ” “from bokeh.resources import INLINE\n”+n”, ” “output_notebook(resources=INLINE)\n”+n”, ” “</code>\n”+n”, ” “</div>”}};n”, “n”, ” function display_loaded() {n”, ” var el = document.getElementById(“1097”);n”, ” if (el != null) {n”, ” el.textContent = “BokehJS is loading…”;n”, ” }n”, ” if (root.Bokeh !== undefined) {n”, ” if (el != null) {n”, ” el.textContent = “BokehJS ” + root.Bokeh.version + ” successfully loaded.”;n”, ” }n”, ” } else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(display_loaded, 100)n”, ” }n”, ” }n”, “n”, “n”, ” function run_callbacks() {n”, ” try {n”, ” root._bokeh_onload_callbacks.forEach(function(callback) {n”, ” if (callback != null)n”, ” callback();n”, ” });n”, ” } finally {n”, ” delete root._bokeh_onload_callbacksn”, ” }n”, ” console.debug(“Bokeh: all callbacks have finished”);n”, ” }n”, “n”, ” function load_libs(css_urls, js_urls, callback) {n”, ” if (css_urls == null) css_urls = [];n”, ” if (js_urls == null) js_urls = [];n”, “n”, ” root._bokeh_onload_callbacks.push(callback);n”, ” if (root._bokeh_is_loading > 0) {n”, ” console.debug(“Bokeh: BokehJS is being loaded, scheduling callback at”, now());n”, ” return null;n”, ” }n”, ” if (js_urls == null || js_urls.length === 0) {n”, ” run_callbacks();n”, ” return null;n”, ” }n”, ” console.debug(“Bokeh: BokehJS not loaded, scheduling load and callback at”, now());n”, ” root._bokeh_is_loading = css_urls.length + js_urls.length;n”, “n”, ” function on_load() {n”, ” root._bokeh_is_loading–;n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: all BokehJS libraries/stylesheets loaded”);n”, ” run_callbacks()n”, ” }n”, ” }n”, “n”, ” function on_error() {n”, ” console.error(“failed to load ” + url);n”, ” }n”, “n”, ” for (var i = 0; i < css_urls.length; i++) {n”, ” var url = css_urls[i];n”, ” const element = document.createElement(“link”);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.rel = “stylesheet”;n”, ” element.type = “text/css”;n”, ” element.href = url;n”, ” console.debug(“Bokeh: injecting link tag for BokehJS stylesheet: “, url);n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” for (var i = 0; i < js_urls.length; i++) {n”, ” var url = js_urls[i];n”, ” var element = document.createElement(‘script’);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.async = false;n”, ” element.src = url;n”, ” console.debug(“Bokeh: injecting script tag for BokehJS library: “, url);n”, ” document.head.appendChild(element);n”, ” }n”, ” };var element = document.getElementById(“1097”);n”, ” if (element == null) {n”, ” console.error(“Bokeh: ERROR: autoload.js configured with elementid ‘1097’ but no matching script tag was found. “)n”, ” return false;n”, ” }n”, “n”, ” function inject_raw_css(css) {n”, ” const element = document.createElement(“style”);n”, ” element.appendChild(document.createTextNode(css));n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” n”, ” var js_urls = [”https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js”];n”, ” var css_urls = [];n”, ” n”, “n”, ” var inline_js = [n”, ” function(Bokeh) {n”, ” Bokeh.set_log_level(“info”);n”, ” },n”, ” function(Bokeh) {n”, ” n”, ” n”, ” }n”, ” ];n”, “n”, ” function run_inline_js() {n”, ” n”, ” if (root.Bokeh !== undefined || force === true) {n”, ” n”, ” for (var i = 0; i < inline_js.length; i++) {n”, ” inline_js[i].call(root, root.Bokeh);n”, ” }n”, ” if (force === true) {n”, ” display_loaded();n”, ” }} else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(run_inline_js, 100);n”, ” } else if (!root._bokeh_failed_load) {n”, ” console.log(“Bokeh: BokehJS failed to load within specified timeout.”);n”, ” root._bokeh_failed_load = true;n”, ” } else if (force !== true) {n”, ” var cell = $(document.getElementById(“1097”)).parents(‘.cell’).data().cell;n”, ” cell.output_area.append_execute_result(NB_LOAD_WARNING)n”, ” }n”, “n”, ” }n”, “n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: BokehJS loaded, going straight to plotting”);n”, ” run_inline_js();n”, ” } else {n”, ” load_libs(css_urls, js_urls, function() {n”, ” console.debug(“Bokeh: BokehJS plotting callback run at”, now());n”, ” run_inline_js();n”, ” });n”, ” }n”, “}(window));”

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Application”,”version”:”1.4.0”}};n”, ” var render_items = [{“docid”:”ab0455ad-e672-4804-93c6-1175501f9f29”,”roots”:{“1098”:”e3e98465-db3d-400a-943f-d21e6916c9e5”}}];n”, ” root.Bokeh.embed.embed_items_notebook(docs_json, render_items);n”, “n”, ” }n”, ” if (root.Bokeh !== undefined) {n”, ” embed_document(root);n”, ” } else {n”, ” var attempts = 0;n”, ” var timer = setInterval(function(root) {n”, ” if (root.Bokeh !== undefined) {n”, ” clearInterval(timer);n”, ” embed_document(root);n”, ” } else {n”, ” attempts++;n”, ” if (attempts > 100) {n”, ” clearInterval(timer);n”, ” console.log(“Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing”);n”, ” }n”, ” }n”, ” }, 10, root)n”, ” }n”, “})(window);”

], “application/vnd.bokehjs_exec.v0+json”: “”

}, “metadata”: {

“application/vnd.bokehjs_exec.v0+json”: {
“id”: “1098”

}

}, “output_type”: “display_data”

}

], “source”: [

“bokeh.io.output_notebook()n”, “n”, “# Assigning datan”, “stream1 = channel_raw_data.recordings[0].analog_streams[0]n”, “stream2 = channel_raw_data.recordings[0].analog_streams[1]n”, “channel_id = list(channel_raw_data.recordings[0].analog_streams[1].channel_infos.keys())[0]n”, “n”, “# Defining rangen”, “time1 = stream1.get_channel_sample_timestamps(channel_id,0,3000)n”, “signal1 = stream1.get_channel_in_range(channel_id,0,3000)n”, “time2 = stream2.get_channel_sample_timestamps(channel_id,0,3000)n”, “signal2 = stream2.get_channel_in_range(channel_id,0,3000)n”, “n”, “# Bokeh-Plotn”, “bfig = bokeh.plotting.figure(plot_width=900, plot_height=400, title=’Sampled signal overlay '%s' and '%s'’ % (stream1.label, stream2.label))n”, “bfig.multi_line(n”, ” xs = [time1[0], time2[0]],n”, ” ys = [signal1[0], signal2[0]],n”, ” line_color = Spectral11[0:2],n”, ” alpha = 0.8n”, “)n”, “bfig.xaxis.axis_label = ‘Time (%s)’ % time1[1]n”, “bfig.yaxis.axis_label = ‘Voltage (%s)’ % signal1[1]n”, “bfig.ygrid.minor_grid_line_color = ‘navy’n”, “bfig.ygrid.minor_grid_line_alpha = 0.1n”, “bokeh.plotting.show(bfig)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“n”, “#### Heatmap of activity:n”, “n”, “Heatmaps are another way of displaying raw data like it’s stored in AnalogStream_1. n”, “n”, “In this example all data from all channels are accessed by calling .channel_data[:, 0:10000].”

]

}, {

“cell_type”: “code”, “execution_count”: 25, “metadata”: {}, “outputs”: [

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 1440x864 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“data = channel_raw_data.recordings[0].analog_streams[1].channel_data[:, 0:10000]n”, “aspect_ratio = 1000n”, “n”, “plt.figure(figsize=(20,12))n”, “plt.set_cmap(“jet”)n”, “plt.imshow(data, interpolation=’nearest’, aspect=aspect_ratio)n”, “plt.xlabel(‘Sample Index’)n”, “plt.ylabel(‘Channel Number’)n”, “plt.title(‘Heatmap of sampled wireless Signal’)n”, “plt.show()”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

” <a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### FrameStream<a id=’FS’></a>n”, “n”, “FrameStreams are a representation of the signals recorded by the chip of a CMOS-MEA system. n”, “n”, “The following examples demonstrate how to access all or just some of the data in respect to sensor position on the CMOS chip or timeframe of interest.n”, “n”, “We start of by setting the raw_data to an HDF5 file containing some FrameStream data.n”

]

}, {

“cell_type”: “code”, “execution_count”: 26, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Recording_0 <HDF5 group “/Data/Recording_0” (1 members)>n”, “Stream_0 <HDF5 group “/Data/Recording_0/FrameStream/Stream_0” (2 members)>n”, “FrameDataEntity_0 <HDF5 group “/Data/Recording_0/FrameStream/Stream_0/FrameDataEntity_0” (3 members)>n”, “InfoFrame <HDF5 dataset “InfoFrame”: shape (1,), type “|V128”>n”, “<McsPy.McsData.FrameStream object at 0x0000016BFE5EC6D8>n”

]

}

], “source”: [

“frame_raw_data = McsPy.McsData.RawData(os.path.join(test_data_folder, ‘CMOSTestRec.h5’))n”, “n”, “print(frame_raw_data.recordings[0].frame_streams[0])”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“We can see that we have one Stream with data included in our FrameStream and inside of that we have one FrameDataEntity which holds Data:n”, “n”, ” ‘Stream_0’n”, ” ->’FrameDataEntity_0’n”, ” n”, “For FrameStreams the ID instead of the index has to be used when accessing the data of an entity. Just like in the example for AnalogStreams we can look at the IDs by calling .keys() on all entities.”

]

}, {

“cell_type”: “code”, “execution_count”: 27, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“dict_keys([1])n”

]

}

], “source”: [

“print(frame_raw_data.recordings[0].frame_streams[0].frame_entity.keys())”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“With info.info additional info can be accessed of the desired entity (frame_entity[1]).”

]

}, {

“cell_type”: “code”, “execution_count”: 28, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“{‘FrameID’: 1, ‘FrameDataID’: 0, ‘GroupID’: 1, ‘Label’: ‘ROI 1’, ‘RawDataType’: ‘Short’, ‘Unit’: ‘V’, ‘Exponent’: -9, ‘ADZero’: 0, ‘Tick’: 50, ‘HighPassFilterType’: ‘’, ‘HighPassFilterCutOffFrequency’: ‘-1’, ‘HighPassFilterOrder’: -1, ‘LowPassFilterType’: ‘’, ‘LowPassFilterCutOffFrequency’: ‘-1’, ‘LowPassFilterOrder’: -1, ‘SensorSpacing’: 1, ‘FrameLeft’: 1, ‘FrameTop’: 1, ‘FrameRight’: 65, ‘FrameBottom’: 65, ‘ReferenceFrameLeft’: 1, ‘ReferenceFrameTop’: 1, ‘ReferenceFrameRight’: 65, ‘ReferenceFrameBottom’: 65}n”

]

}

], “source”: [

“print(frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].info.info)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Keys of the dictionary can be used to access the values like this:”

]

}, {

“cell_type”: “code”, “execution_count”: 29, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Vn”

]

}

], “source”: [

“print(frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].info.info[‘Unit’])”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Of course the values aren’t in Volt but the unit has to be adjusted with the ‘Exponent’. Here, the value of -9 indicates that the values are given in µV.”

]

}, {

“cell_type”: “code”, “execution_count”: 30, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“-9n”

]

}

], “source”: [

“print(frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].info.info[‘Exponent’])”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The following code shows how the data and specific parts of it can be accessed by using .data and handing over a range of rows, columns, and frames.n”, “n”, “All rows and all columns of the first frame are: n”, ” n”, ” .data[:,:,0]”

]

}, {

“cell_type”: “code”, “execution_count”: 31, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“<HDF5 dataset “FrameData”: shape (65, 65, 2000), type “<i2”>n”, “n”, “Each “frame” contains: 65 * 65 = 4225 data points.n”, “Each point represents the value of one sensor of the CMOS chip at a given timepoint.n”, “This FrameStream consists of 2000 frames.n”, “n”, “Array of the data contained in the first frame of the stream.n”, ” [[-12 0 22 … 11 11 9]n”, ” [ 16 -14 20 … 21 2 12]n”, ” [ -4 26 27 … 35 -8 -11]n”, ” …n”, ” [-10 12 2 … 3 2 8]n”, ” [ -6 7 -10 … 0 -9 -6]n”, ” [ 0 0 0 … 0 0 0]]n”

]

}

], “source”: [

“frame_data = frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].datan”, “n”, “first_frame = frame_data[:,:,0]n”, “n”, “sensor_calc = str(len(first_frame))+” * “+str(len(first_frame[0]))+” = “+str(len(first_frame)*len(first_frame[0]))n”, “n”, “print(frame_data)n”, “print()n”, “print(“Each "frame" contains: “,sensor_calc,” data points.”)n”, “print(“Each point represents the value of one sensor of the CMOS chip at a given timepoint.”)n”, “print(“This FrameStream consists of”, frame_data.shape[2], “frames.”)n”, “print()n”, “print(“Array of the data contained in the first frame of the stream.\n”,first_frame)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“In order to plot a single frame (the first 65\*65 frame, index 0) we need to extract the data and multiply it by the conversion factors to adjust the values for each sensor.”

]

}, {

“cell_type”: “code”, “execution_count”: 32, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[[1 1 1 … 1 1 1]n”, ” [1 1 1 … 1 1 1]n”, ” [1 1 1 … 1 1 1]n”, ” …n”, ” [1 1 1 … 1 1 1]n”, ” [1 1 1 … 1 1 1]n”, ” [1 1 1 … 1 1 1]]n”

]

}

], “source”: [

“conv_fact = np.array(frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].info.conversion_factors)n”, “n”, “print(conv_fact)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Thanks to numpy’s internals it is able to multiply two arrays of the same dimension like R would.”

]

}, {

“cell_type”: “code”, “execution_count”: 33, “metadata”: {}, “outputs”: [

{
“data”: {
“text/plain”: [
“<matplotlib.image.AxesImage at 0x16bff0096a0>”

]

}, “execution_count”: 33, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: “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”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“f_data = np.array(frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].data[:,:,0])*conv_factn”, “plt.set_cmap(“Greys”) # change colors used by plotn”, “plt.imshow(f_data, interpolation=’none’,vmin=-350000, vmax=350000)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“#### Example of subplotted frames with defined frame interval:”

]

}, {

“cell_type”: “code”, “execution_count”: 34, “metadata”: {}, “outputs”: [

{
“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 1296x432 with 10 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“fig, ((ax1, ax2, ax3, ax4, ax5), ( ax6, ax7, ax8, ax9, ax10)) = plt.subplots(2, 5, sharex=’col’, sharey=’row’)n”, “fig.set_size_inches(18,6)n”, “ax_list = [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9, ax10]n”, “n”, “for i in range(10,20):n”, ” current_frame = np.transpose(frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].data[:,:,i*1])*conv_factn”, ” ax_list[i-10].imshow(current_frame, interpolation=’none’, vmin=-350000, vmax=350000)n”, ” ax_list[i-10].set_axis_off()n”, ” ax_list[i-10].set_title(“Frame: “+str(i*1))n”, ” n”, “plt.show()”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“#### Example of an interactive plot of a FrameStream within a certain time frame:n”, “n”, “With the slider the time point that gets plotted can be defined. This value is handed over to the make_plot function. The interactive ipywidgets slider only works with jupyter notebooks but similar functionalities can be created with matplotlib.widgets or by using graphical modules for python like Tkinter in regular python scripts/apps.n”, “n”, “Plot may “flicker”, a known ipywidgets.interact problem.”

]

}, {

“cell_type”: “code”, “execution_count”: 35, “metadata”: {}, “outputs”: [

{
“data”: {
“text/plain”: [
“<Figure size 576x576 with 0 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “4a1ae3ff845f404da820f86475dde20e”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“interactive(children=(IntSlider(value=0, description=’Frame’, max=1999), Output()), _dom_classes=(‘widget-inte…”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“text/plain”: [
“<function __main__.make_plot(Frame=0)>”

]

}, “execution_count”: 35, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“%matplotlib inlinen”, “import numpy as npn”, “import matplotlib.pyplot as pltn”, “from ipywidgets import interact, HTMLn”, “n”, “fig = plt.figure(figsize=(8,8))n”, “plt.set_cmap(“Greys”) # change colors used by plotn”, “n”, “def make_plot(Frame=0):n”, ” n”, ” ffig = plt.figure(figsize=(8,8))n”, ” ax = plt.gca()n”, ” ax.set_title(“Frame: “+str(Frame))n”, ” current_frame = np.transpose(frame_data[:,:,Frame])*conv_factn”, ” plt.imshow(current_frame, interpolation=’none’, vmin=-350000, vmax=350000)n”, ” plt.colorbar()n”, ” #plt.axis(‘off’)n”, ” return HTML() # said to slightly reduces flickeringn”, ” n”, ” n”, “plt.show()n”, ” n”, “interact(make_plot, Frame=(0, frame_data.shape[2] - 1, 1))”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Viewing the data from a single/multiple sensor/s is also possible.n”, “n”, “The information needed to specify an area on the CMOS chip are the x/y-coordinates of the desired sensors and the indices of the frames which will be viewed. n”, “n”, “In this case x-coords = 40 to 64 and y_coords = 10 to 34 which get defined when defining the range_data variable, n”, “n”, ” range_data = frame_data[30:40,5:15,:]n”, “n”, “and with timestamp indices 0 to 54 which are defined interactively by the slider.”

]

}, {

“cell_type”: “code”, “execution_count”: 36, “metadata”: {}, “outputs”: [

{
“data”: {
“text/plain”: [
“<Figure size 720x720 with 0 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “15d524f7d77e40089a4e1d514f886106”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“interactive(children=(IntSlider(value=0, description=’Frame’, max=54), Output()), _dom_classes=(‘widget-intera…”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“text/plain”: [
“<function __main__.make_plot(Frame=0)>”

]

}, “execution_count”: 36, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“fig = plt.figure(figsize=(10,10))n”, “n”, “range_data = frame_data[40:64,10:34,:]n”, “n”, “def make_plot(Frame=0):n”, ” fig = plt.figure(figsize=(5,5))n”, ” current_frame = np.array(range_data[:,:,Frame])*conv_fact[40:64,10:34]n”, ” plt.imshow(current_frame, interpolation=’none’, vmin=-350000, vmax=350000)n”, ” ax = plt.gca()n”, ” plt.axis(‘off’) # don’t display plot axisn”, ” ax.set_title(“Frame: “+str(Frame))n”, ” return HTML() # slightly reduces flickeringn”, “n”, “plt.show()n”, ” n”, “interact(make_plot, Frame=(0, 54, 1))”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Another way of accessing the data of a single sensor is by using the .get_sensor_signal() function of the frame_entity. n”, “n”, “To do so, one has to provide the x/y-coordinates of the sensor that will get analyzed, as well as the range of indices that will be viewed as arguments to the .get_sensor_signal(sensor_x, sensor_y, idx_start, edx_end) function.n”, “n”, “Additionally to guarantee a correct plot according to the time the data was gathered, the timestamp has to be added by calling .get_frame_timestamps() with the arguments idx_start, idx_end with the same values as the above call for the data.n”

]

}, {

“cell_type”: “code”, “execution_count”: 37, “metadata”: {}, “outputs”: [

{
“data”: {

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n”, “text/plain”: [

“<Figure size 1080x360 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}, {

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 1080x360 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“# Single sensor:n”, “sensor_data = frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].get_sensor_signal(42,7,0,550)n”, “sensor_timestamps = frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].get_frame_timestamps(0,550)n”, “n”, “fig = plt.figure(figsize=(15,5))n”, “plt.plot(sensor_timestamps[0], sensor_data[0], label=”Single sensor”)n”, “plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)n”, “plt.xlabel(‘Time in ms’)n”, “plt.ylabel(‘Voltage in (%s)’ % sensor_data[1])n”, “plt.title(‘Single sensor data’)n”, “plt.show()n”, “n”, “# Multiple single sensors:n”, “sensor_data1 = (frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].get_sensor_signal(40,20,0,550),”Data1”)n”, “sensor_data2 = (frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].get_sensor_signal(50,20,0,550),”Data2”)n”, “sensor_data3 = (frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].get_sensor_signal(60,20,0,550),”Data3”)n”, “n”, “sensor_list = [sensor_data1 ,sensor_data2, sensor_data3]n”, “n”, “n”, “fig = plt.figure(figsize=(15,5))n”, “n”, “for sensor in range(3):n”, ” plt.plot(sensor_timestamps[0], sensor_list[sensor][0][0],label = sensor_list[sensor][1])n”, “n”, “plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)n”, “plt.xlabel(‘Time in ms’)n”, “plt.ylabel(‘Voltage in (%s)’ % sensor_data1[0][1])n”, “plt.title(‘Multiple sensor data’)n”, “plt.show()”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“These plots can also be designed to be interactive:”

]

}, {

“cell_type”: “code”, “execution_count”: 40, “metadata”: {}, “outputs”: [

{
“data”: {
“text/plain”: [
“<Figure size 1080x360 with 0 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “ba91405bbaf54488a315fbb068494432”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“interactive(children=(IntSlider(value=0, description=’Frame’, max=550), Output()), _dom_classes=(‘widget-inter…”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“text/plain”: [
“<function __main__.single_plot(Frame=0)>”

]

}, “execution_count”: 40, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“# Single sensor:n”, “fig = plt.figure(figsize=(15,5))n”, “n”, “def single_plot(Frame=0):n”, ” fig = plt.figure(figsize=(15,5))n”, ” sensor_data = frame_raw_data.recordings[0].frame_streams[0].frame_entity[1].get_sensor_signal(50,20,Frame,Frame+450)n”, ” n”, ” plt.plot(sensor_data[0], label=”Single sensor”)n”, ” plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)n”, ” plt.xlabel(‘Timewindow’)n”, ” plt.ylabel(‘Voltage in (%s)’ % sensor_data[1])n”, ” plt.title(‘Single sensor data, Frame: ‘+str(Frame))n”, ” return HTML() # slightly reduces flickeringn”, “n”, “plt.show()n”, “n”, “interact(single_plot, Frame=(0, 550, 1))n”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“If you happen to have one of our CMOS-MEA systems, feel free to check out our example client written in Python for the CMOS-MEA-Control software, to visualize your experiments in realtime from any PC in your network.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### EventStream<a id=’ES’></a>n”, “n”, “EventStreams can contain wide array of events predefined by the user and stored in this stream. From the beginning/end or the duration of a treatment to periodically recurring stimuli this can be everything.”

]

}, {

“cell_type”: “code”, “execution_count”: 41, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Recording_0 <HDF5 group “/Data/Recording_0” (4 members)>n”, “Stream_0 <HDF5 group “/Data/Recording_0/EventStream/Stream_0” (2 members)>n”, “EventEntity_0 <HDF5 dataset “EventEntity_0”: shape (2, 12), type “<i8”>n”, “InfoEvent <HDF5 dataset “InfoEvent”: shape (1,), type “|V44”>n”, “EventEntity_0 contains: 12 eventsn”, “n”, “All events: (array([[ 216000, 1916000, 3616000, 5316000, 7016000, 8716000,n”, ” 10416000, 12116000, 13814000, 15514000, 17214000, 18914000],n”, ” [ 0, 0, 0, 0, 0, 0,n”, ” 0, 0, 0, 0, 0, 0]],n”, ” dtype=int64), <Unit(‘microsecond’)>)n”, “n”, “[ 216000 1916000 3616000 5316000 7016000 8716000 10416000 12116000n”, ” 13814000 15514000 17214000 18914000]n”, “n”, “[ 216000 1916000 3616000 5316000 7016000 8716000 10416000 12116000n”, ” 13814000 15514000 17214000 18914000]n”, “n”, “[0 0 0 0 0 0 0 0 0 0 0 0]n”

]

}

], “source”: [

“test_raw_data_file_path = os.path.join(test_data_folder, “2014-07-09T10-17-35W8 Standard all 500 Hz.h5”)n”, “n”, “event_raw_data = McsPy.McsData.RawData(test_raw_data_file_path)n”, “n”, “event_entity = event_raw_data.recordings[0].event_streams[0].event_entity[0]n”, “n”, “print(“EventEntity_0 contains: %s events” % event_entity.count)n”, “all_events = event_entity.get_events()n”, “print()n”, “print(“All events: “,all_events)n”, “print()n”, “print(all_events[0][0])n”, “print()n”, “all_event_timestamps = event_entity.get_event_timestamps()n”, “print(all_event_timestamps[0])n”, “print()n”, “all_event_durations = event_entity.get_event_durations()n”, “print(all_event_durations[0])”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“More info with .info.info

]

}, {

“cell_type”: “code”, “execution_count”: 42, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“{‘EventID’: 0, ‘GroupID’: 0, ‘Label’: ‘’, ‘RawDataType’: ‘Int’, ‘RawDataBytes’: 4, ‘SourceChannelIDs’: ‘8’, ‘SourceChannelLabels’: ‘1 \r\n’}n”

]

}

], “source”: [

“print(event_entity.info.info)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Visualization of this information is best combined with other data types to highlight the occurrence of events.n”, “n”, “Depending on the data these plots don’t necessarily overlap.n”, “n”, “First we get the data we want to plot. In this case the data from the AnalogStreams and the data from the TimestampStream is extracted.”

]

}, {

“cell_type”: “code”, “execution_count”: 43, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Stream_0 <HDF5 group “/Data/Recording_0/AnalogStream/Stream_0” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (8, 9850), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (8,), type “|V100”>n”, “Stream_1 <HDF5 group “/Data/Recording_0/AnalogStream/Stream_1” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (8, 9800), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (8,), type “|V100”>n”, “Stream_2 <HDF5 group “/Data/Recording_0/AnalogStream/Stream_2” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (1, 9800), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (1,), type “|V100”>n”, “Stream_0 <HDF5 group “/Data/Recording_0/TimeStampStream/Stream_0” (9 members)>n”, “InfoTimeStamp <HDF5 dataset “InfoTimeStamp”: shape (8,), type “|V44”>n”, “TimeStampEntity_0 <HDF5 dataset “TimeStampEntity_0”: shape (1, 26), type “<i8”>n”, “TimeStampEntity_1 <HDF5 dataset “TimeStampEntity_1”: shape (1, 23), type “<i8”>n”, “TimeStampEntity_2 <HDF5 dataset “TimeStampEntity_2”: shape (1, 30), type “<i8”>n”, “TimeStampEntity_3 <HDF5 dataset “TimeStampEntity_3”: shape (1, 33), type “<i8”>n”, “TimeStampEntity_4 <HDF5 dataset “TimeStampEntity_4”: shape (1, 29), type “<i8”>n”, “TimeStampEntity_5 <HDF5 dataset “TimeStampEntity_5”: shape (1, 28), type “<i8”>n”, “TimeStampEntity_6 <HDF5 dataset “TimeStampEntity_6”: shape (1, 29), type “<i8”>n”, “TimeStampEntity_7 <HDF5 dataset “TimeStampEntity_7”: shape (1, 26), type “<i8”>n”

]

}

], “source”: [

“stream1 = event_raw_data.recordings[0].analog_streams[0]n”, “stream2 = event_raw_data.recordings[0].analog_streams[1]n”, “channel_id = list(event_raw_data.recordings[0].analog_streams[1].channel_infos.keys())[0]n”, “timestamps = event_raw_data.recordings[0].timestamp_streams[0].timestamp_entity[0].get_timestamps()[0]n”, “n”, “time1 = stream1.get_channel_sample_timestamps(channel_id,0,3000)n”, “signal1 = stream1.get_channel_in_range(channel_id,0,3000)n”, “time2 = stream2.get_channel_sample_timestamps(channel_id,0,3000)n”, “signal2 = stream2.get_channel_in_range(channel_id,0,3000)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Next we plot the AnalogStreams according to the Timestamps.n”, “n”, “Then we add some vertical lines representing the Events.n”, “n”, “Finally we define some plot properties and we are done.”

]

}, {

“cell_type”: “code”, “execution_count”: 44, “metadata”: {}, “outputs”: [

{
“data”: {

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n”, “text/plain”: [

“<Figure size 1440x864 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plt.figure(figsize=(20,12))n”, “plt.plot(time1[0], signal1[0])n”, “plt.plot(time2[0], signal2[0])n”, “max_time = max(time1[0][-1],time2[0][-1])n”, “n”, “for event in all_events[0][0]:n”, ” if event < max_time:n”, ” plt.axvline(event, color=’r’) n”, ” n”, “plt.xlabel(‘Time (%s)’ % time1[1])n”, “plt.ylabel(‘Voltage (%s)’ % signal1[1])n”, “plt.title(‘Sampled signal overlay '%s' and '%s'’ % (stream1.label, stream2.label))n”, “plt.show()”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“### SegmentStream<a id=’SS’></a>n”, “n”, “SegmentStreams are further split up into two subtypes:n”, “n”, “- Cutouts: As the name already implies these bits of data are predefined cutouts of a certain dimension from the signal received by the electrodes.n”, “- Averages: Are averages of those cutouts, for all cutouts at certain time pointsn”, “n”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

`python\n", "    python DataStreamInfo.py --f AnalogSegmentTimestamp.h5\n", "`

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“#### Subtype: Cutouts<a id=’SC’></a>n”, “n”, ” Date Program Versionn”, ” ——————- ——— ———n”, ” 2014-07-25 11:30:56 MC_Rack 4.5.12.0n”, “n”, ” Type Stream # chn”, ” ——— ——– ——n”, ” Analog 60n”, ” Segmentn”, ” TimeStampn”, “n”, “For SegmentStreams you can extract a single entity by addressing its index.n”, “n”, “So the first SegmentEntity at index 0 would be:”

]

}, {

“cell_type”: “code”, “execution_count”: 45, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Recording_0 <HDF5 group “/Data/Recording_0” (4 members)>n”, “Stream_0 <HDF5 group “/Data/Recording_0/SegmentStream/Stream_0” (18 members)>n”, “InfoSegment <HDF5 dataset “InfoSegment”: shape (8,), type “|V48”>n”, “SegmentData_0 <HDF5 dataset “SegmentData_0”: shape (2, 26), type “<i4”>n”, “SegmentData_1 <HDF5 dataset “SegmentData_1”: shape (2, 23), type “<i4”>n”, “SegmentData_2 <HDF5 dataset “SegmentData_2”: shape (2, 30), type “<i4”>n”, “SegmentData_3 <HDF5 dataset “SegmentData_3”: shape (2, 33), type “<i4”>n”, “SegmentData_4 <HDF5 dataset “SegmentData_4”: shape (2, 29), type “<i4”>n”, “SegmentData_5 <HDF5 dataset “SegmentData_5”: shape (2, 28), type “<i4”>n”, “SegmentData_6 <HDF5 dataset “SegmentData_6”: shape (2, 29), type “<i4”>n”, “SegmentData_7 <HDF5 dataset “SegmentData_7”: shape (2, 26), type “<i4”>n”, “SegmentData_ts_0 <HDF5 dataset “SegmentData_ts_0”: shape (1, 26), type “<i8”>n”, “SegmentData_ts_1 <HDF5 dataset “SegmentData_ts_1”: shape (1, 23), type “<i8”>n”, “SegmentData_ts_2 <HDF5 dataset “SegmentData_ts_2”: shape (1, 30), type “<i8”>n”, “SegmentData_ts_3 <HDF5 dataset “SegmentData_ts_3”: shape (1, 33), type “<i8”>n”, “SegmentData_ts_4 <HDF5 dataset “SegmentData_ts_4”: shape (1, 29), type “<i8”>n”, “SegmentData_ts_5 <HDF5 dataset “SegmentData_ts_5”: shape (1, 28), type “<i8”>n”, “SegmentData_ts_6 <HDF5 dataset “SegmentData_ts_6”: shape (1, 29), type “<i8”>n”, “SegmentData_ts_7 <HDF5 dataset “SegmentData_ts_7”: shape (1, 26), type “<i8”>n”, “SourceInfoChannel <HDF5 dataset “SourceInfoChannel”: shape (8,), type “|V96”>n”, “n”, “Segment entity 0 contains: 26 segmentsn”

]

}

], “source”: [

“segment_raw_data = McsPy.McsData.RawData(os.path.join(test_data_folder, ‘2014-07-09T10-17-35W8 Standard all 500 Hz.h5’))n”, “n”, “first_segment_entity = segment_raw_data.recordings[0].segment_streams[0].segment_entity[0]n”, “n”, “print()n”, “print(“Segment entity 0 contains: %s segments” % first_segment_entity.segment_sample_count)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Again a full list of of entities to iterate over can be generated with .keys

]

}, {

“cell_type”: “code”, “execution_count”: 46, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“dict_keys([7, 6, 5, 4, 3, 2, 1, 0])n”

]

}

], “source”: [

“segment_stream_keys = segment_raw_data.recordings[0].segment_streams[0].segment_entity.keys()n”, “n”, “print(segment_stream_keys)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The data of one of these entities can either be accessed by .data, but several steps have to be applied for the data to make sense when plotted.”

]

}, {

“cell_type”: “code”, “execution_count”: 47, “metadata”: {}, “outputs”: [], “source”: [

“data = segment_raw_data.recordings[0].segment_streams[0].segment_entity[0].data”

]

}, {

“cell_type”: “code”, “execution_count”: 48, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“52n”

]

}

], “source”: [

“data = np.reshape(data, -1, ‘F’)n”, “print(len(data))”

]

}, {

“cell_type”: “code”, “execution_count”: 49, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“0.00038147n”

]

}

], “source”: [

“scale = segment_raw_data.recordings[0].segment_streams[0].segment_entity[0].info.source_channel_of_segment[0].adc_step.magnituden”, “print(scale)”

]

}, {

“cell_type”: “code”, “execution_count”: 50, “metadata”: {}, “outputs”: [

{
“data”: {
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“n”, ” <div class=”bk-root”>n”, ” <a href=”https://bokeh.org” target=”_blank” class=”bk-logo bk-logo-small bk-logo-notebook”></a>n”, ” <span id=”1201”>Loading BokehJS …</span>n”, ” </div>”

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“n”, “(function(root) {n”, ” function now() {n”, ” return new Date();n”, ” }n”, “n”, ” var force = true;n”, “n”, ” if (typeof root._bokeh_onload_callbacks === “undefined” || force === true) {n”, ” root._bokeh_onload_callbacks = [];n”, ” root._bokeh_is_loading = undefined;n”, ” }n”, “n”, ” var JS_MIME_TYPE = ‘application/javascript’;n”, ” var HTML_MIME_TYPE = ‘text/html’;n”, ” var EXEC_MIME_TYPE = ‘application/vnd.bokehjs_exec.v0+json’;n”, ” var CLASS_NAME = ‘output_bokeh rendered_html’;n”, “n”, ” /n”, ” * Render data to the DOM noden”, ” */n”, ” function render(props, node) {n”, ” var script = document.createElement(“script”);n”, ” node.appendChild(script);n”, ” }n”, “n”, ” /n”, ” * Handle when an output is cleared or removedn”, ” /n”, ” function handleClearOutput(event, handle) {n”, ” var cell = handle.cell;n”, “n”, ” var id = cell.output_area._bokeh_element_id;n”, ” var server_id = cell.output_area._bokeh_server_id;n”, ” // Clean up Bokeh referencesn”, ” if (id != null && id in Bokeh.index) {n”, ” Bokeh.index[id].model.document.clear();n”, ” delete Bokeh.index[id];n”, ” }n”, “n”, ” if (server_id !== undefined) {n”, ” // Clean up Bokeh referencesn”, ” var cmd = “from bokeh.io.state import curstate; print(curstate().uuid_to_server[’” + server_id + “’].get_sessions()[0].document.roots[0]._id)”;n”, ” cell.notebook.kernel.execute(cmd, {n”, ” iopub: {n”, ” output: function(msg) {n”, ” var id = msg.content.text.trim();n”, ” if (id in Bokeh.index) {n”, ” Bokeh.index[id].model.document.clear();n”, ” delete Bokeh.index[id];n”, ” }n”, ” }n”, ” }n”, ” });n”, ” // Destroy server and sessionn”, ” var cmd = “import bokeh.io.notebook as ion; ion.destroy_server(’” + server_id + “’)”;n”, ” cell.notebook.kernel.execute(cmd);n”, ” }n”, ” }n”, “n”, ” /*n”, ” * Handle when a new output is addedn”, ” /n”, ” function handleAddOutput(event, handle) {n”, ” var output_area = handle.output_area;n”, ” var output = handle.output;n”, “n”, ” // limit handleAddOutput to display_data with EXEC_MIME_TYPE content onlyn”, ” if ((output.output_type != “display_data”) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {n”, ” returnn”, ” }n”, “n”, ” var toinsert = output_area.element.find(“.” + CLASS_NAME.split(’ ‘)[0]);n”, “n”, ” if (output.metadata[EXEC_MIME_TYPE][“id”] !== undefined) {n”, ” toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];n”, ” // store reference to embed id on output_arean”, ” output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][“id”];n”, ” }n”, ” if (output.metadata[EXEC_MIME_TYPE][“server_id”] !== undefined) {n”, ” var bk_div = document.createElement(“div”);n”, ” bk_div.innerHTML = output.data[HTML_MIME_TYPE];n”, ” var script_attrs = bk_div.children[0].attributes;n”, ” for (var i = 0; i < script_attrs.length; i++) {n”, ” toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);n”, ” }n”, ” // store reference to server id on output_arean”, ” output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][“server_id”];n”, ” }n”, ” }n”, “n”, ” function register_renderer(events, OutputArea) {n”, “n”, ” function append_mime(data, metadata, element) {n”, ” // create a DOM node to render ton”, ” var toinsert = this.create_output_subarea(n”, ” metadata,n”, ” CLASS_NAME,n”, ” EXEC_MIME_TYPEn”, ” );n”, ” this.keyboard_manager.register_events(toinsert);n”, ” // Render to noden”, ” var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};n”, ” render(props, toinsert[toinsert.length - 1]);n”, ” element.append(toinsert);n”, ” return toinsertn”, ” }n”, “n”, ” / Handle when an output is cleared or removed /n”, ” events.on(‘clear_output.CodeCell’, handleClearOutput);n”, ” events.on(‘delete.Cell’, handleClearOutput);n”, “n”, ” / Handle when a new output is added /n”, ” events.on(‘output_added.OutputArea’, handleAddOutput);n”, “n”, ” /*n”, ” * Register the mime type and append_mime function with output_arean”, ” /n”, ” OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {n”, ” / Is output safe? /n”, ” safe: true,n”, ” / Index of renderer in output_area.display_order */n”, ” index: 0n”, ” });n”, ” }n”, “n”, ” // register the mime type if in Jupyter Notebook environment and previously unregisteredn”, ” if (root.Jupyter !== undefined) {n”, ” var events = require(‘base/js/events’);n”, ” var OutputArea = require(‘notebook/js/outputarea’).OutputArea;n”, “n”, ” if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {n”, ” register_renderer(events, OutputArea);n”, ” }n”, ” }n”, “n”, ” n”, ” if (typeof (root._bokeh_timeout) === “undefined” || force === true) {n”, ” root._bokeh_timeout = Date.now() + 5000;n”, ” root._bokeh_failed_load = false;n”, ” }n”, “n”, ” var NB_LOAD_WARNING = {‘data’: {‘text/html’:n”, ” “<div style=’background-color: #fdd’>\n”+n”, ” “<p>\n”+n”, ” “BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \n”+n”, ” “may be due to a slow or bad network connection. Possible fixes:\n”+n”, ” “</p>\n”+n”, ” “<ul>\n”+n”, ” “<li>re-rerun output_notebook() to attempt to load from CDN again, or</li>\n”+n”, ” “<li>use INLINE resources instead, as so:</li>\n”+n”, ” “</ul>\n”+n”, ” “<code>\n”+n”, ” “from bokeh.resources import INLINE\n”+n”, ” “output_notebook(resources=INLINE)\n”+n”, ” “</code>\n”+n”, ” “</div>”}};n”, “n”, ” function display_loaded() {n”, ” var el = document.getElementById(“1201”);n”, ” if (el != null) {n”, ” el.textContent = “BokehJS is loading…”;n”, ” }n”, ” if (root.Bokeh !== undefined) {n”, ” if (el != null) {n”, ” el.textContent = “BokehJS ” + root.Bokeh.version + ” successfully loaded.”;n”, ” }n”, ” } else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(display_loaded, 100)n”, ” }n”, ” }n”, “n”, “n”, ” function run_callbacks() {n”, ” try {n”, ” root._bokeh_onload_callbacks.forEach(function(callback) {n”, ” if (callback != null)n”, ” callback();n”, ” });n”, ” } finally {n”, ” delete root._bokeh_onload_callbacksn”, ” }n”, ” console.debug(“Bokeh: all callbacks have finished”);n”, ” }n”, “n”, ” function load_libs(css_urls, js_urls, callback) {n”, ” if (css_urls == null) css_urls = [];n”, ” if (js_urls == null) js_urls = [];n”, “n”, ” root._bokeh_onload_callbacks.push(callback);n”, ” if (root._bokeh_is_loading > 0) {n”, ” console.debug(“Bokeh: BokehJS is being loaded, scheduling callback at”, now());n”, ” return null;n”, ” }n”, ” if (js_urls == null || js_urls.length === 0) {n”, ” run_callbacks();n”, ” return null;n”, ” }n”, ” console.debug(“Bokeh: BokehJS not loaded, scheduling load and callback at”, now());n”, ” root._bokeh_is_loading = css_urls.length + js_urls.length;n”, “n”, ” function on_load() {n”, ” root._bokeh_is_loading–;n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: all BokehJS libraries/stylesheets loaded”);n”, ” run_callbacks()n”, ” }n”, ” }n”, “n”, ” function on_error() {n”, ” console.error(“failed to load ” + url);n”, ” }n”, “n”, ” for (var i = 0; i < css_urls.length; i++) {n”, ” var url = css_urls[i];n”, ” const element = document.createElement(“link”);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.rel = “stylesheet”;n”, ” element.type = “text/css”;n”, ” element.href = url;n”, ” console.debug(“Bokeh: injecting link tag for BokehJS stylesheet: “, url);n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” for (var i = 0; i < js_urls.length; i++) {n”, ” var url = js_urls[i];n”, ” var element = document.createElement(‘script’);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.async = false;n”, ” element.src = url;n”, ” console.debug(“Bokeh: injecting script tag for BokehJS library: “, url);n”, ” document.head.appendChild(element);n”, ” }n”, ” };var element = document.getElementById(“1201”);n”, ” if (element == null) {n”, ” console.error(“Bokeh: ERROR: autoload.js configured with elementid ‘1201’ but no matching script tag was found. “)n”, ” return false;n”, ” }n”, “n”, ” function inject_raw_css(css) {n”, ” const element = document.createElement(“style”);n”, ” element.appendChild(document.createTextNode(css));n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” n”, ” var js_urls = [”https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js”];n”, ” var css_urls = [];n”, ” n”, “n”, ” var inline_js = [n”, ” function(Bokeh) {n”, ” Bokeh.set_log_level(“info”);n”, ” },n”, ” function(Bokeh) {n”, ” n”, ” n”, ” }n”, ” ];n”, “n”, ” function run_inline_js() {n”, ” n”, ” if (root.Bokeh !== undefined || force === true) {n”, ” n”, ” for (var i = 0; i < inline_js.length; i++) {n”, ” inline_js[i].call(root, root.Bokeh);n”, ” }n”, ” if (force === true) {n”, ” display_loaded();n”, ” }} else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(run_inline_js, 100);n”, ” } else if (!root._bokeh_failed_load) {n”, ” console.log(“Bokeh: BokehJS failed to load within specified timeout.”);n”, ” root._bokeh_failed_load = true;n”, ” } else if (force !== true) {n”, ” var cell = $(document.getElementById(“1201”)).parents(‘.cell’).data().cell;n”, ” cell.output_area.append_execute_result(NB_LOAD_WARNING)n”, ” }n”, “n”, ” }n”, “n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: BokehJS loaded, going straight to plotting”);n”, ” run_inline_js();n”, ” } else {n”, ” load_libs(css_urls, js_urls, function() {n”, ” console.debug(“Bokeh: BokehJS plotting callback run at”, now());n”, ” run_inline_js();n”, ” });n”, ” }n”, “}(window));”

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“data = data * scalen”, “bokeh.io.output_notebook() # see comment for bokeh module in “Requirements” sectionn”, “bfig = bokeh.plotting.figure(plot_width=900, plot_height=400)n”, “bfig.line(list(range(len(data))),data, alpha=0.8)n”, “bfig.ygrid.minor_grid_line_color = ‘navy’n”, “bfig.ygrid.minor_grid_line_alpha = 0.1n”, “bokeh.plotting.show(bfig)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

.data_ts yields the corresponding timestamps of the segment entity but to be used together similar reformating has to be done.”

]

}, {

“cell_type”: “code”, “execution_count”: 51, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

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]

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“data”: {
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“n”, ” <div class=”bk-root”>n”, ” <a href=”https://bokeh.org” target=”_blank” class=”bk-logo bk-logo-small bk-logo-notebook”></a>n”, ” <span id=”1318”>Loading BokehJS …</span>n”, ” </div>”

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If loading BokehJS from CDN, this \n”+n”, ” “may be due to a slow or bad network connection. Possible fixes:\n”+n”, ” “</p>\n”+n”, ” “<ul>\n”+n”, ” “<li>re-rerun output_notebook() to attempt to load from CDN again, or</li>\n”+n”, ” “<li>use INLINE resources instead, as so:</li>\n”+n”, ” “</ul>\n”+n”, ” “<code>\n”+n”, ” “from bokeh.resources import INLINE\n”+n”, ” “output_notebook(resources=INLINE)\n”+n”, ” “</code>\n”+n”, ” “</div>”}};n”, “n”, ” function display_loaded() {n”, ” var el = document.getElementById(“1318”);n”, ” if (el != null) {n”, ” el.textContent = “BokehJS is loading…”;n”, ” }n”, ” if (root.Bokeh !== undefined) {n”, ” if (el != null) {n”, ” el.textContent = “BokehJS ” + root.Bokeh.version + ” successfully loaded.”;n”, ” }n”, ” } else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(display_loaded, 100)n”, ” }n”, ” }n”, “n”, “n”, ” function run_callbacks() {n”, ” try {n”, ” root._bokeh_onload_callbacks.forEach(function(callback) {n”, ” if (callback != null)n”, ” callback();n”, ” });n”, ” } finally {n”, ” delete root._bokeh_onload_callbacksn”, ” }n”, ” console.debug(“Bokeh: all callbacks have finished”);n”, ” }n”, “n”, ” function load_libs(css_urls, js_urls, callback) {n”, ” if (css_urls == null) css_urls = [];n”, ” if (js_urls == null) js_urls = [];n”, “n”, ” root._bokeh_onload_callbacks.push(callback);n”, ” if (root._bokeh_is_loading > 0) {n”, ” console.debug(“Bokeh: BokehJS is being loaded, scheduling callback at”, now());n”, ” return null;n”, ” }n”, ” if (js_urls == null || js_urls.length === 0) {n”, ” run_callbacks();n”, ” return null;n”, ” }n”, ” console.debug(“Bokeh: BokehJS not loaded, scheduling load and callback at”, now());n”, ” root._bokeh_is_loading = css_urls.length + js_urls.length;n”, “n”, ” function on_load() {n”, ” root._bokeh_is_loading–;n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: all BokehJS libraries/stylesheets loaded”);n”, ” run_callbacks()n”, ” }n”, ” }n”, “n”, ” function on_error() {n”, ” console.error(“failed to load ” + url);n”, ” }n”, “n”, ” for (var i = 0; i < css_urls.length; i++) {n”, ” var url = css_urls[i];n”, ” const element = document.createElement(“link”);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.rel = “stylesheet”;n”, ” element.type = “text/css”;n”, ” element.href = url;n”, ” console.debug(“Bokeh: injecting link tag for BokehJS stylesheet: “, url);n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” for (var i = 0; i < js_urls.length; i++) {n”, ” var url = js_urls[i];n”, ” var element = document.createElement(‘script’);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.async = false;n”, ” element.src = url;n”, ” console.debug(“Bokeh: injecting script tag for BokehJS library: “, url);n”, ” document.head.appendChild(element);n”, ” }n”, ” };var element = document.getElementById(“1318”);n”, ” if (element == null) {n”, ” console.error(“Bokeh: ERROR: autoload.js configured with elementid ‘1318’ but no matching script tag was found. “)n”, ” return false;n”, ” }n”, “n”, ” function inject_raw_css(css) {n”, ” const element = document.createElement(“style”);n”, ” element.appendChild(document.createTextNode(css));n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” n”, ” var js_urls = [”https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js”];n”, ” var css_urls = [];n”, ” n”, “n”, ” var inline_js = [n”, ” function(Bokeh) {n”, ” Bokeh.set_log_level(“info”);n”, ” },n”, ” function(Bokeh) {n”, ” n”, ” n”, ” }n”, ” ];n”, “n”, ” function run_inline_js() {n”, ” n”, ” if (root.Bokeh !== undefined || force === true) {n”, ” n”, ” for (var i = 0; i < inline_js.length; i++) {n”, ” inline_js[i].call(root, root.Bokeh);n”, ” }n”, ” if (force === true) {n”, ” display_loaded();n”, ” }} else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(run_inline_js, 100);n”, ” } else if (!root._bokeh_failed_load) {n”, ” console.log(“Bokeh: BokehJS failed to load within specified timeout.”);n”, ” root._bokeh_failed_load = true;n”, ” } else if (force !== true) {n”, ” var cell = $(document.getElementById(“1318”)).parents(‘.cell’).data().cell;n”, ” cell.output_area.append_execute_result(NB_LOAD_WARNING)n”, ” }n”, “n”, ” }n”, “n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: BokehJS loaded, going straight to plotting”);n”, ” run_inline_js();n”, ” } else {n”, ” load_libs(css_urls, js_urls, function() {n”, ” console.debug(“Bokeh: BokehJS plotting callback run at”, now());n”, ” run_inline_js();n”, ” });n”, ” }n”, “}(window));”

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{n”, ” if (root.Bokeh !== undefined) {n”, ” clearInterval(timer);n”, ” embed_document(root);n”, ” } else {n”, ” attempts++;n”, ” if (attempts > 100) {n”, ” clearInterval(timer);n”, ” console.log(“Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing”);n”, ” }n”, ” }n”, ” }, 10, root)n”, ” }n”, “})(window);”

], “application/vnd.bokehjs_exec.v0+json”: “”

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], “source”: [

“signal = first_segment_entity.get_segment_in_range(segment_id = 0, flat = True)n”, “n”, “print(signal[0])n”, “print(signal[1])n”, “n”, “bokeh.io.output_notebook() # see comment for bokeh module in “Requirements” sectionn”, “bfig = bokeh.plotting.figure(plot_width=900, plot_height=400)n”, “bfig.line(list(range(len(data))),data, alpha=0.8)n”, “bfig.xaxis.axis_label = ‘Sample Index’n”, “bfig.yaxis.axis_label = ‘Voltage (%s)’ % signal1[1]n”, “bfig.ygrid.minor_grid_line_color = ‘navy’n”, “bfig.ygrid.minor_grid_line_alpha = 0.1n”, “bokeh.plotting.show(bfig)”

]

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“cell_type”: “markdown”, “metadata”: {}, “source”: [

“As you can see the above steps are rather complicated. Therefore custom functions have been already implemented in McsPy to make your life easier: n”, “n”, “get_segment_in_range() n”, “n”, “andn”, “n”, “get_segment_sample_timestamps()n”, “n”, “n”, “With this built-in function one can select ranges of these segments included in the SegmentEntities. n”, “n”, “If you want to plot the included data to quickly visualize it you need two things:n”, “n”, “1. the signal itselfn”, “2. the corresponding timestampn”, “n”, “For this we can use get_segment_in_range() and get_segment_sample_timestamps(). Arguments that can be passed are:n”, “n”, “- segment_id: Id of the SegmentData within the Entity that will be analyzedn”, “- flat: False will leave data dimensions unchanged, True will convert data into a one-dimensional vector of the sequentially ordered segmentsn”, “- idx_start: Index of the first segment that should be returned. If left unspecified will be first possible index.n”, “- idx_end: Index of the last segment that should be returned If left unspecified will be last possible index.n”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“The parameter flat needs to be set to True, so the data is flattened into a one-dimensional array and matplotlib’s plot function can handle it.n”, “n”, “Overlaying data from all segments might look like this:”

]

}, {

“cell_type”: “code”, “execution_count”: 75, “metadata”: {}, “outputs”: [

{
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“n”, ” <div class=”bk-root”>n”, ” <a href=”https://bokeh.org” target=”_blank” class=”bk-logo bk-logo-small bk-logo-notebook”></a>n”, ” <span id=”5285”>Loading BokehJS …</span>n”, ” </div>”

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If loading BokehJS from CDN, this \n”+n”, ” “may be due to a slow or bad network connection. Possible fixes:\n”+n”, ” “</p>\n”+n”, ” “<ul>\n”+n”, ” “<li>re-rerun output_notebook() to attempt to load from CDN again, or</li>\n”+n”, ” “<li>use INLINE resources instead, as so:</li>\n”+n”, ” “</ul>\n”+n”, ” “<code>\n”+n”, ” “from bokeh.resources import INLINE\n”+n”, ” “output_notebook(resources=INLINE)\n”+n”, ” “</code>\n”+n”, ” “</div>”}};n”, “n”, ” function display_loaded() {n”, ” var el = document.getElementById(“5285”);n”, ” if (el != null) {n”, ” el.textContent = “BokehJS is loading…”;n”, ” }n”, ” if (root.Bokeh !== undefined) {n”, ” if (el != null) {n”, ” el.textContent = “BokehJS ” + root.Bokeh.version + ” successfully loaded.”;n”, ” }n”, ” } else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(display_loaded, 100)n”, ” }n”, ” }n”, “n”, “n”, ” function run_callbacks() {n”, ” try {n”, ” root._bokeh_onload_callbacks.forEach(function(callback) {n”, ” if (callback != null)n”, ” callback();n”, ” });n”, ” } finally {n”, ” delete root._bokeh_onload_callbacksn”, ” }n”, ” console.debug(“Bokeh: all callbacks have finished”);n”, ” }n”, “n”, ” function load_libs(css_urls, js_urls, callback) {n”, ” if (css_urls == null) css_urls = [];n”, ” if (js_urls == null) js_urls = [];n”, “n”, ” root._bokeh_onload_callbacks.push(callback);n”, ” if (root._bokeh_is_loading > 0) {n”, ” console.debug(“Bokeh: BokehJS is being loaded, scheduling callback at”, now());n”, ” return null;n”, ” }n”, ” if (js_urls == null || js_urls.length === 0) {n”, ” run_callbacks();n”, ” return null;n”, ” }n”, ” console.debug(“Bokeh: BokehJS not loaded, scheduling load and callback at”, now());n”, ” root._bokeh_is_loading = css_urls.length + js_urls.length;n”, “n”, ” function on_load() {n”, ” root._bokeh_is_loading–;n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: all BokehJS libraries/stylesheets loaded”);n”, ” run_callbacks()n”, ” }n”, ” }n”, “n”, ” function on_error() {n”, ” console.error(“failed to load ” + url);n”, ” }n”, “n”, ” for (var i = 0; i < css_urls.length; i++) {n”, ” var url = css_urls[i];n”, ” const element = document.createElement(“link”);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.rel = “stylesheet”;n”, ” element.type = “text/css”;n”, ” element.href = url;n”, ” console.debug(“Bokeh: injecting link tag for BokehJS stylesheet: “, url);n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” for (var i = 0; i < js_urls.length; i++) {n”, ” var url = js_urls[i];n”, ” var element = document.createElement(‘script’);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.async = false;n”, ” element.src = url;n”, ” console.debug(“Bokeh: injecting script tag for BokehJS library: “, url);n”, ” document.head.appendChild(element);n”, ” }n”, ” };var element = document.getElementById(“5285”);n”, ” if (element == null) {n”, ” console.error(“Bokeh: ERROR: autoload.js configured with elementid ‘5285’ but no matching script tag was found. “)n”, ” return false;n”, ” }n”, “n”, ” function inject_raw_css(css) {n”, ” const element = document.createElement(“style”);n”, ” element.appendChild(document.createTextNode(css));n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” n”, ” var js_urls = [”https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js”];n”, ” var css_urls = [];n”, ” n”, “n”, ” var inline_js = [n”, ” function(Bokeh) {n”, ” Bokeh.set_log_level(“info”);n”, ” },n”, ” function(Bokeh) {n”, ” n”, ” n”, ” }n”, ” ];n”, “n”, ” function run_inline_js() {n”, ” n”, ” if (root.Bokeh !== undefined || force === true) {n”, ” n”, ” for (var i = 0; i < inline_js.length; i++) {n”, ” inline_js[i].call(root, root.Bokeh);n”, ” }n”, ” if (force === true) {n”, ” display_loaded();n”, ” }} else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(run_inline_js, 100);n”, ” } else if (!root._bokeh_failed_load) {n”, ” console.log(“Bokeh: BokehJS failed to load within specified timeout.”);n”, ” root._bokeh_failed_load = true;n”, ” } else if (force !== true) {n”, ” var cell = $(document.getElementById(“5285”)).parents(‘.cell’).data().cell;n”, ” cell.output_area.append_execute_result(NB_LOAD_WARNING)n”, ” }n”, “n”, ” }n”, “n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: BokehJS loaded, going straight to plotting”);n”, ” run_inline_js();n”, ” } else {n”, ” load_libs(css_urls, js_urls, function() {n”, ” console.debug(“Bokeh: BokehJS plotting callback run at”, now());n”, ” run_inline_js();n”, ” });n”, ” }n”, “}(window));”

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setInterval(function(root) {n”, ” if (root.Bokeh !== undefined) {n”, ” clearInterval(timer);n”, ” embed_document(root);n”, ” } else {n”, ” attempts++;n”, ” if (attempts > 100) {n”, ” clearInterval(timer);n”, ” console.log(“Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing”);n”, ” }n”, ” }n”, ” }, 10, root)n”, ” }n”, “})(window);”

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], “source”: [

“bokeh.io.output_notebook()n”, “n”, “signal_ts = first_segment_entity.get_segment_sample_timestamps(segment_id = 0, flat = True)n”, “n”, “factor = ureg.convert(1, str(signal_ts[1]), “second”)n”, “signal_ts_second = signal_ts[0] * factorn”, “segments = [segment_raw_data.recordings[0].segment_streams[0].segment_entity[i].get_segment_in_range(segment_id = 0, flat = True)[0] for i in segment_raw_data.recordings[0].segment_streams[0].segment_entity.keys()]n”, “timestamps = [segment_raw_data.recordings[0].segment_streams[0].segment_entity[i].get_segment_sample_timestamps(segment_id = 0, flat = True)[0]*factor for i in segment_raw_data.recordings[0].segment_streams[0].segment_entity.keys()]n”, “# Bokeh-Plotn”, “palette=Spectral11[0:len(segments)]n”, “bfig = bokeh.plotting.figure(plot_width=900, plot_height=400, title=’Sampled Signal Segments’)n”, “bfig.multi_line(n”, ” xs = timestamps,n”, ” ys = segments,n”, ” line_color=palette,n”, ” alpha = 0.8n”, “)n”, “#bfig.line(signal_ts_second, segment_raw_data.recordings[0].segment_streams[0].segment_entity[i].get_segment_in_range(segment_id = 0, flat = True)[0], alpha=0.8)n”, “bfig.xaxis.axis_label = ‘Time (%s)’ % ureg.sn”, “bfig.yaxis.axis_label = ‘Voltage (%s)’ % signal1[1]n”, “bfig.ygrid.minor_grid_line_color = ‘navy’n”, “bfig.ygrid.minor_grid_line_alpha = 0.1n”, “bokeh.plotting.show(bfig)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“#### Subtype: Averages <a id=’SA’></a>n”, “n”, “Averages are a convenient built-in way to get precalculated values for mean and standard deviation of a collection of predefined sensors/timeframes.n”, “n”, “Calling DataStreamInfo.py on AverageEvent.h5 reveals its contentn”, “n”, ” Date Program Versionn”, ” ——————- —————- ———n”, ” 2015-04-02 16:04:26 Multiwell-Screen 1.2.1.0n”, “n”, ” Type Stream # chn”, ” ——- ———————————- ——n”, ” Event Experiment State Changes_00 Atriumn”, ” Event Applied Dilution Series_00 Atriumn”, ” Segment Averages_00 Atriumn”, ” n”, “The file has three Streams: Two EventStreams which, in this case, hold information about state changes of the experiment aswell as applied dilutions and one AverageStream, in this case holding data from an experiment mith cardiac muscel cells. n”, “n”, “Data access looks like this:”

]

}, {

“cell_type”: “code”, “execution_count”: 77, “metadata”: {}, “outputs”: [], “source”: [

“average_raw_data = McsPy.McsData.RawData(os.path.join(test_data_folder, “20150402_00 Atrium_002.h5”))”

]

}, {

“cell_type”: “code”, “execution_count”: 78, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Recording_0 <HDF5 group “/Data/Recording_0” (2 members)>n”, “Stream_0 <HDF5 group “/Data/Recording_0/SegmentStream/Stream_0” (24 members)>n”, “AverageData_18 <HDF5 dataset “AverageData_18”: shape (2, 5100, 8), type “<i4”>n”, “AverageData_19 <HDF5 dataset “AverageData_19”: shape (2, 5100, 8), type “<i4”>n”, “AverageData_21 <HDF5 dataset “AverageData_21”: shape (2, 5100, 9), type “<i4”>n”, “AverageData_30 <HDF5 dataset “AverageData_30”: shape (2, 5100, 9), type “<i4”>n”, “AverageData_31 <HDF5 dataset “AverageData_31”: shape (2, 5100, 9), type “<i4”>n”, “AverageData_33 <HDF5 dataset “AverageData_33”: shape (2, 5100, 7), type “<i4”>n”, “AverageData_38 <HDF5 dataset “AverageData_38”: shape (2, 5100, 6), type “<i4”>n”, “AverageData_40 <HDF5 dataset “AverageData_40”: shape (2, 5100, 6), type “<i4”>n”, “AverageData_42 <HDF5 dataset “AverageData_42”: shape (2, 5100, 8), type “<i4”>n”, “AverageData_43 <HDF5 dataset “AverageData_43”: shape (2, 5100, 8), type “<i4”>n”, “AverageData_45 <HDF5 dataset “AverageData_45”: shape (2, 5100, 9), type “<i4”>n”, “AverageData_Range_18 <HDF5 dataset “AverageData_Range_18”: shape (3, 8), type “<i8”>n”, “AverageData_Range_19 <HDF5 dataset “AverageData_Range_19”: shape (3, 8), type “<i8”>n”, “AverageData_Range_21 <HDF5 dataset “AverageData_Range_21”: shape (3, 9), type “<i8”>n”, “AverageData_Range_30 <HDF5 dataset “AverageData_Range_30”: shape (3, 9), type “<i8”>n”, “AverageData_Range_31 <HDF5 dataset “AverageData_Range_31”: shape (3, 9), type “<i8”>n”, “AverageData_Range_33 <HDF5 dataset “AverageData_Range_33”: shape (3, 7), type “<i8”>n”, “AverageData_Range_38 <HDF5 dataset “AverageData_Range_38”: shape (3, 6), type “<i8”>n”, “AverageData_Range_40 <HDF5 dataset “AverageData_Range_40”: shape (3, 6), type “<i8”>n”, “AverageData_Range_42 <HDF5 dataset “AverageData_Range_42”: shape (3, 8), type “<i8”>n”, “AverageData_Range_43 <HDF5 dataset “AverageData_Range_43”: shape (3, 8), type “<i8”>n”, “AverageData_Range_45 <HDF5 dataset “AverageData_Range_45”: shape (3, 9), type “<i8”>n”, “InfoSegment <HDF5 dataset “InfoSegment”: shape (288,), type “|V48”>n”, “SourceInfoChannel <HDF5 dataset “SourceInfoChannel”: shape (288,), type “|V100”>n”

]

}

], “source”: [

“average_data = average_raw_data.recordings[0].segment_streams[0]”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“As you can see the the entities are not consecutively numbered by id and not consecutively by index. To be able to iterate over all entities of the stream we have to get a list of indices.n”, “n”, “By looking at how McsData.py accesses the HDF5 file we know that upon initialization of the stream a dictionary is created with IDs and values. With pythons .keys() we can create a list of all entity IDs.”

]

}, {

“cell_type”: “code”, “execution_count”: 79, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[18, 19, 21, 30, 31, 33, 38, 40, 42, 43, 45]n”

]

}

], “source”: [

“id_list = average_data.segment_entity.keys()n”, “n”, “id_list = sorted(id_list)n”, “n”, “print(id_list)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Just like addressing FrameStreamEntities instead of an index we need to provide the ID of the entity, in this case one of the electrodes within a single well, we want to analyze. Let’s pick 31 from the index_list we just generated.”

]

}, {

“cell_type”: “code”, “execution_count”: 80, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“<HDF5 dataset “AverageData_31”: shape (2, 5100, 9), type “<i4”>n”

]

}

], “source”: [

“average_data_31 = average_raw_data.recordings[0].segment_streams[0].segment_entity[31].datan”, “n”, “print(average_data_31)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“When looking at the data with HDFView 2.11 we see how the data is arranged within the file. It has 2 rows, row 0 holds the mean values and row 2 holds the values for standard deviation, 5100 columns representing time points of measurement, and 9 sheets, in this case representing different treatments.”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“So to get the mean values (index 0), all of them (index 0 to index 5100) of the first treatment (index 0) and plot these:”

]

}, {

“cell_type”: “code”, “execution_count”: 81, “metadata”: {}, “outputs”: [

{
“data”: {
“text/plain”: [
“<function matplotlib.pyplot.show(*args, **kw)>”

]

}, “execution_count”: 81, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

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”, “text/plain”: [

“<Figure size 864x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plt.figure(figsize=(12,6))n”, “n”, “plt.plot(average_data_31[0,0:5100,0])n”, “n”, “plt.show”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“Plotting all 9 treatments/sheets could look like this:”

]

}, {

“cell_type”: “code”, “execution_count”: 84, “metadata”: {}, “outputs”: [

{
“data”: {
“text/html”: [
“n”, ” <div class=”bk-root”>n”, ” <a href=”https://bokeh.org” target=”_blank” class=”bk-logo bk-logo-small bk-logo-notebook”></a>n”, ” <span id=”6747”>Loading BokehJS …</span>n”, ” </div>”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“application/javascript”: [
“n”, “(function(root) {n”, ” function now() {n”, ” return new Date();n”, ” }n”, “n”, ” var force = true;n”, “n”, ” if (typeof root._bokeh_onload_callbacks === “undefined” || force === true) {n”, ” root._bokeh_onload_callbacks = [];n”, ” root._bokeh_is_loading = undefined;n”, ” }n”, “n”, ” var JS_MIME_TYPE = ‘application/javascript’;n”, ” var HTML_MIME_TYPE = ‘text/html’;n”, ” var EXEC_MIME_TYPE = ‘application/vnd.bokehjs_exec.v0+json’;n”, ” var CLASS_NAME = ‘output_bokeh rendered_html’;n”, “n”, ” /n”, ” * Render data to the DOM noden”, ” */n”, ” function render(props, node) {n”, ” var script = document.createElement(“script”);n”, ” node.appendChild(script);n”, ” }n”, “n”, ” /n”, ” * Handle when an output is cleared or removedn”, ” /n”, ” function handleClearOutput(event, handle) {n”, ” var cell = handle.cell;n”, “n”, ” var id = cell.output_area._bokeh_element_id;n”, ” var server_id = cell.output_area._bokeh_server_id;n”, ” // Clean up Bokeh referencesn”, ” if (id != null && id in Bokeh.index) {n”, ” Bokeh.index[id].model.document.clear();n”, ” delete Bokeh.index[id];n”, ” }n”, “n”, ” if (server_id !== undefined) {n”, ” // Clean up Bokeh referencesn”, ” var cmd = “from bokeh.io.state import curstate; print(curstate().uuid_to_server[’” + server_id + “’].get_sessions()[0].document.roots[0]._id)”;n”, ” cell.notebook.kernel.execute(cmd, {n”, ” iopub: {n”, ” output: function(msg) {n”, ” var id = msg.content.text.trim();n”, ” if (id in Bokeh.index) {n”, ” Bokeh.index[id].model.document.clear();n”, ” delete Bokeh.index[id];n”, ” }n”, ” }n”, ” }n”, ” });n”, ” // Destroy server and sessionn”, ” var cmd = “import bokeh.io.notebook as ion; ion.destroy_server(’” + server_id + “’)”;n”, ” cell.notebook.kernel.execute(cmd);n”, ” }n”, ” }n”, “n”, ” /*n”, ” * Handle when a new output is addedn”, ” /n”, ” function handleAddOutput(event, handle) {n”, ” var output_area = handle.output_area;n”, ” var output = handle.output;n”, “n”, ” // limit handleAddOutput to display_data with EXEC_MIME_TYPE content onlyn”, ” if ((output.output_type != “display_data”) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {n”, ” returnn”, ” }n”, “n”, ” var toinsert = output_area.element.find(“.” + CLASS_NAME.split(’ ‘)[0]);n”, “n”, ” if (output.metadata[EXEC_MIME_TYPE][“id”] !== undefined) {n”, ” toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];n”, ” // store reference to embed id on output_arean”, ” output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][“id”];n”, ” }n”, ” if (output.metadata[EXEC_MIME_TYPE][“server_id”] !== undefined) {n”, ” var bk_div = document.createElement(“div”);n”, ” bk_div.innerHTML = output.data[HTML_MIME_TYPE];n”, ” var script_attrs = bk_div.children[0].attributes;n”, ” for (var i = 0; i < script_attrs.length; i++) {n”, ” toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);n”, ” }n”, ” // store reference to server id on output_arean”, ” output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][“server_id”];n”, ” }n”, ” }n”, “n”, ” function register_renderer(events, OutputArea) {n”, “n”, ” function append_mime(data, metadata, element) {n”, ” // create a DOM node to render ton”, ” var toinsert = this.create_output_subarea(n”, ” metadata,n”, ” CLASS_NAME,n”, ” EXEC_MIME_TYPEn”, ” );n”, ” this.keyboard_manager.register_events(toinsert);n”, ” // Render to noden”, ” var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};n”, ” render(props, toinsert[toinsert.length - 1]);n”, ” element.append(toinsert);n”, ” return toinsertn”, ” }n”, “n”, ” / Handle when an output is cleared or removed /n”, ” events.on(‘clear_output.CodeCell’, handleClearOutput);n”, ” events.on(‘delete.Cell’, handleClearOutput);n”, “n”, ” / Handle when a new output is added /n”, ” events.on(‘output_added.OutputArea’, handleAddOutput);n”, “n”, ” /*n”, ” * Register the mime type and append_mime function with output_arean”, ” /n”, ” OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {n”, ” / Is output safe? /n”, ” safe: true,n”, ” / Index of renderer in output_area.display_order */n”, ” index: 0n”, ” });n”, ” }n”, “n”, ” // register the mime type if in Jupyter Notebook environment and previously unregisteredn”, ” if (root.Jupyter !== undefined) {n”, ” var events = require(‘base/js/events’);n”, ” var OutputArea = require(‘notebook/js/outputarea’).OutputArea;n”, “n”, ” if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {n”, ” register_renderer(events, OutputArea);n”, ” }n”, ” }n”, “n”, ” n”, ” if (typeof (root._bokeh_timeout) === “undefined” || force === true) {n”, ” root._bokeh_timeout = Date.now() + 5000;n”, ” root._bokeh_failed_load = false;n”, ” }n”, “n”, ” var NB_LOAD_WARNING = {‘data’: {‘text/html’:n”, ” “<div style=’background-color: #fdd’>\n”+n”, ” “<p>\n”+n”, ” “BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \n”+n”, ” “may be due to a slow or bad network connection. Possible fixes:\n”+n”, ” “</p>\n”+n”, ” “<ul>\n”+n”, ” “<li>re-rerun output_notebook() to attempt to load from CDN again, or</li>\n”+n”, ” “<li>use INLINE resources instead, as so:</li>\n”+n”, ” “</ul>\n”+n”, ” “<code>\n”+n”, ” “from bokeh.resources import INLINE\n”+n”, ” “output_notebook(resources=INLINE)\n”+n”, ” “</code>\n”+n”, ” “</div>”}};n”, “n”, ” function display_loaded() {n”, ” var el = document.getElementById(“6747”);n”, ” if (el != null) {n”, ” el.textContent = “BokehJS is loading…”;n”, ” }n”, ” if (root.Bokeh !== undefined) {n”, ” if (el != null) {n”, ” el.textContent = “BokehJS ” + root.Bokeh.version + ” successfully loaded.”;n”, ” }n”, ” } else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(display_loaded, 100)n”, ” }n”, ” }n”, “n”, “n”, ” function run_callbacks() {n”, ” try {n”, ” root._bokeh_onload_callbacks.forEach(function(callback) {n”, ” if (callback != null)n”, ” callback();n”, ” });n”, ” } finally {n”, ” delete root._bokeh_onload_callbacksn”, ” }n”, ” console.debug(“Bokeh: all callbacks have finished”);n”, ” }n”, “n”, ” function load_libs(css_urls, js_urls, callback) {n”, ” if (css_urls == null) css_urls = [];n”, ” if (js_urls == null) js_urls = [];n”, “n”, ” root._bokeh_onload_callbacks.push(callback);n”, ” if (root._bokeh_is_loading > 0) {n”, ” console.debug(“Bokeh: BokehJS is being loaded, scheduling callback at”, now());n”, ” return null;n”, ” }n”, ” if (js_urls == null || js_urls.length === 0) {n”, ” run_callbacks();n”, ” return null;n”, ” }n”, ” console.debug(“Bokeh: BokehJS not loaded, scheduling load and callback at”, now());n”, ” root._bokeh_is_loading = css_urls.length + js_urls.length;n”, “n”, ” function on_load() {n”, ” root._bokeh_is_loading–;n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: all BokehJS libraries/stylesheets loaded”);n”, ” run_callbacks()n”, ” }n”, ” }n”, “n”, ” function on_error() {n”, ” console.error(“failed to load ” + url);n”, ” }n”, “n”, ” for (var i = 0; i < css_urls.length; i++) {n”, ” var url = css_urls[i];n”, ” const element = document.createElement(“link”);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.rel = “stylesheet”;n”, ” element.type = “text/css”;n”, ” element.href = url;n”, ” console.debug(“Bokeh: injecting link tag for BokehJS stylesheet: “, url);n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” for (var i = 0; i < js_urls.length; i++) {n”, ” var url = js_urls[i];n”, ” var element = document.createElement(‘script’);n”, ” element.onload = on_load;n”, ” element.onerror = on_error;n”, ” element.async = false;n”, ” element.src = url;n”, ” console.debug(“Bokeh: injecting script tag for BokehJS library: “, url);n”, ” document.head.appendChild(element);n”, ” }n”, ” };var element = document.getElementById(“6747”);n”, ” if (element == null) {n”, ” console.error(“Bokeh: ERROR: autoload.js configured with elementid ‘6747’ but no matching script tag was found. “)n”, ” return false;n”, ” }n”, “n”, ” function inject_raw_css(css) {n”, ” const element = document.createElement(“style”);n”, ” element.appendChild(document.createTextNode(css));n”, ” document.body.appendChild(element);n”, ” }n”, “n”, ” n”, ” var js_urls = [”https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js”, “https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js”];n”, ” var css_urls = [];n”, ” n”, “n”, ” var inline_js = [n”, ” function(Bokeh) {n”, ” Bokeh.set_log_level(“info”);n”, ” },n”, ” function(Bokeh) {n”, ” n”, ” n”, ” }n”, ” ];n”, “n”, ” function run_inline_js() {n”, ” n”, ” if (root.Bokeh !== undefined || force === true) {n”, ” n”, ” for (var i = 0; i < inline_js.length; i++) {n”, ” inline_js[i].call(root, root.Bokeh);n”, ” }n”, ” if (force === true) {n”, ” display_loaded();n”, ” }} else if (Date.now() < root._bokeh_timeout) {n”, ” setTimeout(run_inline_js, 100);n”, ” } else if (!root._bokeh_failed_load) {n”, ” console.log(“Bokeh: BokehJS failed to load within specified timeout.”);n”, ” root._bokeh_failed_load = true;n”, ” } else if (force !== true) {n”, ” var cell = $(document.getElementById(“6747”)).parents(‘.cell’).data().cell;n”, ” cell.output_area.append_execute_result(NB_LOAD_WARNING)n”, ” }n”, “n”, ” }n”, “n”, ” if (root._bokeh_is_loading === 0) {n”, ” console.debug(“Bokeh: BokehJS loaded, going straight to plotting”);n”, ” run_inline_js();n”, ” } else {n”, ” load_libs(css_urls, js_urls, function() {n”, ” console.debug(“Bokeh: BokehJS plotting callback run at”, now());n”, ” run_inline_js();n”, ” });n”, ” }n”, “}(window));”

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