Welcome to the McsPyDataTools documentation!

The aim of the McsPyDataTools package is to provide a convenient python interface to access the content of HDF5 data files created by the Multi Channel DataManager and other Multi Channel Systems MCS GmbH software.

McsPyDataTools API Reference

McsPy

McsPy is a Python module/package to read, handle and operate on HDF5-based raw data files converted from recordings of devices of the Multi Channel Systems MCS GmbH.

copyright:

3. 2022 by Multi Channel Systems MCS GmbH

license:

see LICENSE for more details

class McsPy.McsHdf5Protocols

Class of supported MCS-HDF5 protocol types and version ranges

Entry: (Protocol Type Name => Tuple of supported version range from (including) the first version entry up to (including) the second version entry)

classmethod check_hdf5_protocol_version(protocol_name, version)

Check if the given version of the HDF5 protocol is supported

Parameters:

• protocol_name – name of the protocol that is tested
• version – version number that should be checked

Returns:

is true if the given protocol and version is supported

classmethod check_protocol_type_version(protocol_type_name, version)

Check if the given version of a protocol is supported by the implementation

Parameters:

• protocol_type_name – name of the protocol that is tested
• version – version number that should be checked

Returns:

is true if the given protocol and version is supported

class McsPy.McsHdf5Types

Class of supported MCS-HDF5 file structure types and version ranges

Entry: (Protocol TypeID => Tuple of supported version range from (including) the first version entry up to (including) the second version entry)

classmethod get_mcs_class_name(typeID)

Returns the McsPy class name, that corresponds to a given Mcs HDF5 file structure type. The function also checks if the requested class supports the Mcs HDF5 file structure type version

Parameters:typeID – name of the type that is tested Returns:a McsCMOSMEA class if the given type and version is supported

The ‘’McsData’’ module

class McsPy.McsData.RawData(raw_data_path)

This class holds the information of a complete MCS raw data file

Parameters:

• raw_data_path – path to the HDF5 file
• h5_file – h5py File handle
• mcs_hdf5_protocol_type – protocol type. Currently, only “RawData” is supported
• mcs_hdf5_protocol_type_version – protocol version
• comment – comment string
• clr_date – recording date string
• date_in_clr_ticks – recording date in CLR ticks (100 ns)
• date – datetime object representing the recording date
• file_guid – file GUID
• mea_layout – name of the MEA layout
• mea_sn – serial number of the MEA
• mea_name – name of the MEA
• program_name – name of the recording software that created the file
• program_version – version of the recording software that created the file

recordings

Access recordings

class McsPy.McsData.Recording(recording_grp)

Container class for one recording

Parameters:

• comment – recording comment
• duration – duration of the recording in microseconds
• label – recording label
• recording_id – recording ID
• recording_type – recording type
• timestamp – recording timestamp

Provides the content of the HDF5 Folder “Recording_x” in Python.

analog_streams

Access all analog streams - collection of AnalogStream objects

frame_streams

Access all frame streams - collection of FrameStream objects

event_streams

Access event streams - collection of EventStream objects

segment_streams

Access segment streams - collection of SegementStream objects

timestamp_streams

Access timestamp streams - collection of TimestampStream objects

duration_time

Duration of the recording

Data-Stream-Structures containing the data

class McsPy.McsData.Stream(stream_grp, info_type_name=None)

Base class for all stream types

class McsPy.McsData.AnalogStream(stream_grp)

Container class for one analog stream of several channels. Description for each channel is provided by a channel-associated object of ChannelInfo

Parameters:

• channel_data – numpy array (channels x samples) with channel data
• timestamp_index – numpy array (segment x 3) defining the timestamps for each data segment as [first_timestamp, first_index, last_index]. Interpretation: All samples in channel_data between first_index and last_index (inclusive) are in a continuous data segment and the timestamp of the samples at first_index is first_timestamp
• channel_infos – dict with channel metadata. Key: ChannelID, Value: ChannelInfo object
• info_version – version of the Info structure
• data_subtype – type of stream data (“Electrode”, “Auxiliary”, “Digital”, …)
• label – the stream label
• source_stream_guid – the GUID of the source stream
• stream_guid – the stream GUID
• stream_type – the stream type (“Analog”)

Provides the content of the HDF5 Sub-folder “Stream_x” of “AnalogStream” in Python.

get_channel(channel_id)

Get the signal of the given channel over the course of time and in its measured range.

Parameters:channel_id – ID of the channel Returns:Tuple (vector of the signal, unit of the values)

get_channel_in_range(channel_id, idx_start=0, idx_end=None)

Get the signal of the given channel over the course of time and in its measured range.

Parameters:

• channel_id – ID of the channel
• idx_start – index of the first sampled signal value that should be returned (0 <= idx_start < idx_end <= count samples). Default: 0
• idx_end – index of the last sampled signal value that should be returned (0 <= idx_start < idx_end <= count samples). Default: None (= last index)

Returns:

Tuple (vector of the signal, unit of the values)

get_channel_sample_timestamps(channel_id, idx_start=0, idx_end=None)

Get the timestamps of the sampled values.

Parameters:

• channel_id – ID of the channel
• idx_start – index of the first signal timestamp that should be returned (0 <= idx_start < idx_end <= count samples). Default: 0
• idx_end – index of the last signal timestamp that should be returned (0 <= idx_start < idx_end <= count samples). Default: None (= last index)

Returns:

Tuple (vector of the timestamps, unit of the timestamps)

class McsPy.McsData.FrameStream(stream_grp)

Container class for one frame stream with different entities

Parameters:

• frame_entity – list of FrameEntity objects
• info_version – version of the Info structure
• data_subtype – type of stream data
• label – the stream label
• source_stream_guid – the GUID of the source stream
• stream_guid – the stream GUID
• stream_type – the stream type (“Frame”)

Provides the content of the HDF5 Subfolder “Stream_x” of “FrameStream” in Python.

class McsPy.McsData.FrameEntity(frame_entity_group, frame_info)

Contains the stream of a specific frame entity. Meta-Information for this entity is available via an associated object of FrameEntityInfo

Parameters:

• data – numpy array (sensors_X x sensors_Y x time) of sensor data in ADC steps. Multiply with info.conversion_factors to get voltages.
• info – FrameEntityInfo object with Frame metadata
• timestamp_index – start timestamp of the Frame

Provides the content of the HDF5 Subfolder “Stream_x” of “FrameStream” and Subfolder “FrameDataEntity_x” in Python.

get_sensor_signal(sensor_x, sensor_y, idx_start, idx_end)

Get the signal of a single sensor over the curse of time and in its measured range.

Parameters:

• sensor_x – x coordinate of the sensor
• sensor_y – y coordinate of the sensor
• idx_start – index of the first sampled frame that should be returned (0 <= idx_start < idx_end <= count frames)
• idx_end – index of the last sampled frame that should be returned (0 <= idx_start < idx_end <= count frames)

Returns:

Tuple (vector of the signal, unit of the values)

get_frame_timestamps(idx_start, idx_end)

Get the timestamps of the sampled frames.

Parameters:

• idx_start – index of the first sampled frame that should be returned (0 <= idx_start < idx_end <= count frames)
• idx_end – index of the last sampled frame that should be returned (0 <= idx_start < idx_end <= count frames)

Returns:

Tuple (vector of the timestamps, unit of the timestamps)

class McsPy.McsData.EventStream(stream_grp)

Container class for one event stream with different entities

Parameters:

• event_entity – dict of event entities. Key: event ID, value: a EventEntity object
• info_version – version of the Info structure
• data_subtype – type of stream data (“DigitalInput”, “UserData”, …)
• label – the stream label
• source_stream_guid – the GUID of the source stream
• stream_guid – the stream GUID
• stream_type – the stream type (“Event”)

Provides the content of the HDF5 Subfolder “Stream_x” of “EventStream” in Python.

class McsPy.McsData.EventEntity(event_data, event_info)

Contains the event data of a specific entity. Meta-Information for this entity is available via an associated object of EventEntityInfo

Parameters:

• data – numpy array (5 x n_events) of event data. row 0: event timestamp in microsecond; row 1: event duration in microseconds; row 2: event type; row 3-4: reserved
• info – EventEntityInfo object with the event metadata

Maps data event entity content of the HDF5 Subfolder “Stream_x” of “EventStream” to Python structures.

count

Number of contained events

get_events(idx_start=None, idx_end=None)

Get all n events of this entity of the given index range (idx_start <= idx < idx_end)

Parameters:

• idx_start – start index of the range (including), if nothing is given -> 0
• idx_end – end index of the range (excluding, if nothing is given -> last index

Returns:

Tuple of (2 x n matrix of timestamp (1. row) and duration (2. row), Used unit of time)

get_event_timestamps(idx_start=None, idx_end=None)

Get all n event timestamps of this entity of the given index range

Parameters:

• idx_start – start index of the range, if nothing is given -> 0
• idx_end – end index of the range, if nothing is given -> last index

Returns:

Tuple of (n-length array of timestamps, Used unit of time)

get_event_durations(idx_start=None, idx_end=None)

Get all n event durations of this entity of the given index range

Parameters:

• idx_start – start index of the range, if nothing is given -> 0
• idx_end – end index of the range, if nothing is given -> last index

Returns:

Tuple of (n-length array of duration, Used unit of time)

class McsPy.McsData.SegmentStream(stream_grp)

Container class for one segment stream of different segment entities

Parameters:

• segment_entity – dict of segment entities. Key: segment ID, value: for data_subtype == “Cutout”: SegmentEntity object, for data_subtype == “Average: AverageSegmentEntity object
• info_version – version of the Info structure
• data_subtype – type of stream data (“Cutout”, “Average”, …)
• label – the stream label
• source_stream_guid – the GUID of the source stream
• stream_guid – the stream GUID
• stream_type – the stream type (“Segment”)

Provides the content of the HDF5 Subfolder “Stream_x” of “SegmentStream” in Python.

class McsPy.McsData.SegmentEntity(segment_data, segment_ts, segment_info)

Segment entity class, Meta-Information for this entity is available via an associated object of SegmentEntityInfo

Parameters:

• data – numpy array (n_segments x samples) or (n_segments x n_multi x samples) with segment data
• data_ts – numpy vector (n_segments) with timestamps for every segment
• info –

  segment metadata as a SegmentEntityInfo object

  DataSubType != Average → Maps segement entity content of the HDF5 Subfolder “Stream_x” of “SegmentStream” to Python structures.

segment_sample_count

Number of contained samples of segments (2d) or multi-segments (3d)

segment_count

Number of segments that are sampled for one time point (2d) -> 1 and (3d) -> n

get_segment_in_range(segment_id, flat=False, idx_start=None, idx_end=None)

Get the a/the segment signals in its measured range.

Parameters:

• segment_id – id resp. number of the segment (0 if only one segment is present or the index inside the multi-segment collection)
• flat – true -> one-dimensional vector of the sequentially ordered segments, false -> k x n matrix of the n segments of k sample points
• idx_start – index of the first segment that should be returned (0 <= idx_start < idx_end <= count segments)
• idx_end – index of the last segment that should be returned (0 <= idx_start < idx_end <= count segments)

Returns:

Tuple (of a flat vector of the sequentially ordered segments or a k x n matrix of the n segments of k sample points depending on the value of flat , and the unit of the values)

get_segment_sample_timestamps(segment_id, flat=False, idx_start=None, idx_end=None)

Get the timestamps of the sample points of the measured segment.

Parameters:

• segment_id – id resp. number of the segment (0 if only one segment is present or the index inside the multi-segment collection)
• flat – true -> one-dimensional vector of the sequentially ordered segment timestamps, false -> k x n matrix of the k timestamps of n segments
• idx_start – index of the first segment for that timestamps should be returned (0 <= idx_start < idx_end <= count segments)
• idx_end – index of the last segment for that timestamps should be returned (0 <= idx_start < idx_end <= count segments)

Returns:

Tuple (of a flat vector of the sequentially ordered segments or a k x n matrix of the n segments of k sample points depending on the value of flat , and the unit of the values)

class McsPy.McsData.AverageSegmentTuple(mean, std_dev, time_tick_unit, signal_unit)

Named tuple that describe one or more average segments (mean, std_dev, time_tick_unit, signal_unit).

Note

• mean - mean signal values
• std_dev - standard deviation of the signal value (it is 0 if there was only one sample segment)
• time_tick_unit - sampling interval with time unit
• signal_unit - measured unit of the signal

mean

Alias for field number 0

signal_unit

Alias for field number 3

std_dev

Alias for field number 1

time_tick_unit

Alias for field number 2

class McsPy.McsData.AverageSegmentEntity(segment_average_data, segment_average_annotation, segment_info)

Contains a number of signal segments that are calcualted as averages of number of segments occured in a given time range. Meta-Information for this entity is available via an associated object of SegmentEntityInfo

Parameters:

• data – numpy array (2 x n_samples x n_averages) with averages and standard deviations. First row is the mean per sample data, second row is the standard deviation per sample data.
• data_annotation – numpy array (3 x n_averages) with metadata describing how each average was created. row 0 and 1: start and end timestamp in microseconds of the time window in which signal segments were averaged. row 2: number of averaged signal segments in this time window
• info – metadata for the average as a SegmentEntityInfo object

DataSubType == Average → Maps segment entity content of the HDF5 DataSubType-Average: Subfolder “Stream_x” of “SegmentStream” to Python structures.

number_of_averages

Number of average segments inside this average entity

sample_length

Number of sample points of an average segment

time_ranges()

List of time range tuples for all contained average segments

Returns:List of tuple with start and end time point

time_range(average_segment_idx)

Get the time range for that the average segment was calculated

Parameters:average_segment_idx – index resp. number of the average segment Returns:Tuple with start and end time point

average_counts()

List of counts of samples for all contained average segments

Parameters:average_segment_idx – id resp. number of the average segment Returns:sample count

average_count(average_segment_idx)

Count of samples that were used to calculate the average

Parameters:average_segment_idx – id resp. number of the average segment Returns:sample count

get_scaled_average_segments()

Get all contained average segments in its measured physical range.

Returns:AverageSegmentTuple containing the k x n matrices for mean and standard deviation of all contained average segments n with the associated sampling and measuring information

get_scaled_average_segment(average_segment_idx)

Get the selected average segment in its measured physical range.

Parameters:segment_idx – index resp. number of the average segment Returns:AverageSegmentTuple containing the mean and standard deviation vector of the average segment with the associated sampling and measuring information

get_average_segments()

Get all contained average segments AD-offset in ADC values with its measuring conditions

Returns:AverageSegmentTuple containing the mean and standard deviation vector of the average segment in ADC steps with sampling tick and ADC-Step definition

get_average_segment(average_segment_idx)

Get the AD-offset corrected average segment in ADC values with its measuring conditions

Parameters:segment_id – id resp. number of the segment Returns:AverageSegmentTuple containing the k x n matrices for mean and standard deviation of all contained average segments in ADC steps with sampling tick and ADC-Step definition

class McsPy.McsData.TimeStampStream(stream_grp)

Container class for one timestamp stream with different entities

Parameters:

• timestamp_entity – dict of timestamp entities. Key: timestamp ID, value: a TimeStampEntity object
• info_version – version of the Info structure
• data_subtype – type of stream data
• label – the stream label
• source_stream_guid – the GUID of the source stream
• stream_guid – the stream GUID
• stream_type – the stream type (“Timestamp”)

Provides the content of the HDF5 Subfolder “Stream_x” of “TimeStampStream” in Python.

class McsPy.McsData.TimeStampEntity(timestamp_data, timestamp_info)

Time-Stamp entity class, Meta-Information for this entity is available via an associated object of TimestampEntityInfo

Parameters:

• data – numpy vector (n_timestamps) with timestamp data
• info – timestamp metadata as a TimeStampEntityInfo object

Maps data timestamp entity data of the HDF5 Subfolder “Stream_x” of “TimeStampStream” to Python structures.

count

Number of contained timestamps

get_timestamps(idx_start=None, idx_end=None)

Get all n time stamps of this entity of the given index range (idx_start <= idx < idx_end)

Parameters:

• idx_start – start index of the range (including), if nothing is given -> 0
• idx_end – end index of the range (excluding, if nothing is given -> last index

Returns:

Tuple of (n-length array of timestamps, Used unit of time)

Info-Classes containing Meta-Information for the data

class McsPy.McsData.Info(info_data)

Base class of all info classes

Derived classes contain meta information for data structures and fields.

get_field(name)

Get the field with that name -> access to the raw info array

group_id

Get the id of the group that the objects belongs to

label

Label of this object

data_type

Raw data type of this object

class McsPy.McsData.ChannelInfo(info_version, info)

Contains all describing meta data for one sampled channel

Parameters:info – dict containing the channel metadata

channel_id

Get the ID of the channel

row_index

Get the index of the row that contains the associated channel data inside the data matrix

adc_step

Size and unit of one ADC step for this channel

version

Version number of the Type-Definition

class McsPy.McsData.InfoSampledData(info)

Base class of all info classes for evenly sampled data

sampling_frequency

Get the used sampling frequency in Hz

sampling_tick

Get the used sampling tick

class McsPy.McsData.EventEntityInfo(info_version, info)

Contains all meta data for one event entity

Parameters:info – dict containing the event metadata

id

Event ID

raw_data_bytes

Length of raw data in bytes

source_channel_ids

IDs of all channels that were involved in the event generation.

source_channel_labels

Labels of the channels that were involved in the event generation.

version

Version number of the type definition

class McsPy.McsData.SegmentEntityInfo(info_version, info, source_channel_infos)

Contains all meta data for one segment entity

Parameters:

• info – dict containing the segment metadata
• source_channel_of_segment – dict containing the channel metadata for each source channel of the segment. Key: source channel index, value: ChannelInfo object

id

Segment ID

pre_interval

Interval [start of the segment <- defining event timestamp]

post_interval

Interval [defining event timestamp -> end of the segment]

type

Type of the segment like ‘Average’ or ‘Cutout’

count

Count of segments inside the segment entity

version

Version number of the Type-Definition

class McsPy.McsData.TimeStampEntityInfo(info_version, info)

Contains all meta data for one timestamp entity

Parameters:info – dict containing the timestamp metadata

id

Timestamp entity ID

unit

Unit in which the timestamps are measured

exponent

Exponent for the unit in which the timestamps are measured

measuring_unit

Unit in which the timestamp entity was measured

data_type

DataType for the timestamps

source_channel_ids

ID’s of all channels that were involved in the timestamp generation.

source_channel_labels

Labels of the channels that were involved in the timestamp generation.

version

Version number of the Type-Definition

The ‘’McsCMOSMEA’’ module

class McsPy.McsCMOSMEA.McsCMOSMEAData(cmos_data_path)

This class holds the information of a complete MCS CMOS-MEA data file system

classmethod sensorID_to_coordinates(sensorID)

Computes the [x,y] chip coordinates of a sensor. Note: both, sensor IDs and coordinates are base 1

classmethod coordinates_to_sensorID(row: int, col: int) → int

Computes the sensor ID for row and column coordinates. Note: sensor IDs and rows and columns are base 1

class McsPy.McsCMOSMEA.McsGroup(h5py_group_object)

this class subclasses the h5py.Group object and extends it with McsPy toolbox functionality

class IDSetGroup(h5py, mcs_instanceid, mcspy, mcs_typeid)

h5py

Alias for field number 0

mcs_instanceid

Alias for field number 1

mcs_typeid

Alias for field number 3

mcspy

Alias for field number 2

class IDSetDataset(h5py, mcs_instanceid, mcspy, mcs_typeid)

h5py

Alias for field number 0

mcs_instanceid

Alias for field number 1

mcs_typeid

Alias for field number 3

mcspy

Alias for field number 2

ischild(id)

Takes an identifier and checks if it is a valid identifier for a child of this group:

Parameters:id – mcs instanceid, h5py name , mcspy name as instance of ‘str’ Returns:False if id is not valid, set of identifiers of the child

tree(name='mcspy', mcs_type=False, max_level=None)

builds the hdf5 hierarchy beginning with the current group then traversing all subentities depth first as a string

Parameters:

• name – cfg variable for the type of name that is to be printed for each entity in the h5py group, default: ‘h5py’, options: ‘mcspy’
• mcs_type – cfg variable to show mcs type in the tree, default: False
• max_level – cfg variable to limit the number of tree levels shown, default: None (show all)

class McsPy.McsCMOSMEA.McsDataset(h5py_dataset_object)

This class subclasses the h5py.Dataset object and extends it with McsPy toolbox functionality

iscompound()

Determines whether Dataset is a Compound Dataset

Return Boolean:True if Dataset object represents h5py Compound Dataset, False otherwise

to_pdDataFrame()

Returns the data set as a pandas DataFrame

Raw Data (.cmcr) files

class McsPy.McsCMOSMEA.Acquisition(acquisition_group)

Container class for acquisition data.

Acquisition Group can hold different types of streams: Analog Streams, Event Streams, Timestamp Streams, Segment Streams, Spike Streams

class McsPy.McsCMOSMEA.McsInfo(meta_data_set)

Container class for Stream Meta Data

class McsPy.McsCMOSMEA.McsStream(stream_grp, data_typeid, meta_typeid, *args)

Base class for all stream types

Data

Access all datasets - collection of McsDataset objects

Meta

Access meta data

class McsPy.McsCMOSMEA.McsChannelStream(channel_stream_grp)

Container class for one analog stream of several channels.

DataChunk

The continuous data segments in the stream

class McsPy.McsCMOSMEA.McsChannelEntity(channel_stream_entity_dataset, mcspy_parent)

Container class for one ChannelStream Entity.

Meta

reads the subset of Meta data that belongs to the channels

class McsPy.McsCMOSMEA.McsEventStream(event_stream_grp)

Container class for one Event Stream.

EventData

All events of all event entities in the stream

EventMeta

The meta data for all event entities

EventEntity

All event entities in the stream

class McsPy.McsCMOSMEA.McsEventEntity(parent, event_id)

Container class for Event Entity object

events

The ids, timestamps and durations of the occurences of the event entity

meta

The meta data for an event entity

class McsPy.McsCMOSMEA.McsSensorStream(sensor_stream_grp)

Container class for one Event Stream.

DataChunk

The groups of data that have been acquired. Intended for acquisition of multiple time windows

Regions

The regions of interest (ROI) on the sensor for which data has been acquired, usually from a rectangular subset of the sensors

SensorData

The sensor data as a numpy array of shape (frames x sensors_Y x sensors_X)

SensorMeta

The meta data for the acquired sensor data

class McsPy.McsCMOSMEA.McsSensorEntity(sensor_stream_entity_dataset, mcspy_parent)

Container class for one McsSensorEntity - a sensor stream entity.

class McsPy.McsCMOSMEA.McsSpikeStream(spike_stream_grp, spike_data_typeid='3e8aaacc-268b-4057-b0bb-45d7dc9ec73b')

Container class for one Spike Stream.

get_spikes_at_sensor(sensor_id)

retrieves all spikes that occured at the sensor with id sensor_id

Parameters:sensor_id – valid identifier for a sensor on the MCS CMOS chip as int: 1 <= sensor_id <= 65*65 Returns:numpy structured array of all spikes that have been detected on the sensor with id sensor_id

get_spikes_in_interval(interval)

Retrieves all spikes that occured in a given time interval. Intervals exceeding the time range of the dataset will throw a warning, and retrieval of maximally sized subset of the interval is attempted.

Parameters:interval –

interval in s as instance of

• list(start,stop) of length 2
• tuple(start,stop) of length 2

start must be a number, stop must be a number or the keyword ‘end’, start and stop must satisfy start < stop

Result:numpy structured array which includes all spikes occuring in the given interval

get_spike_timestamps_at_sensors(sensor_ids)

Retrieves all spike timestamps for all given sensors as a dictionary

Parameters:sensor_ids – valid identifiers for sensors on the MCS CMOS chip as int: 1 <= sensor_id <= 65*65 Returns:dictionary of all spike timestamps that have been detected on the given sensors. Key: sensor_id, value: spike timestamps

get_spike_cutouts_at_sensor(sensor_id)

Retrieves the spike cutouts for all spikes for the given sensor_id

Parameters:sensor_id – valid identifier for a sensor on the MCS CMOS chip as int: 1 <= sensor_id <= 65*65 Returns:Numpy array spikes x samples of the spike cutouts

SpikeData

The detected spikes, each with a sensor ID, a timestamp and (optionally) with a cutout

SpikeMeta

The meta data for spike detection, e.g. pre- and post interval

class McsPy.McsCMOSMEA.McsSpikeEntity(spike_stream_entity_dataset, mcspy_parent)

Container class for one SpikeStream Entity.

class McsPy.McsCMOSMEA.McsSegmentStream(segment_stream_grp)

Container class for one segment stream of different segment entities

class McsPy.McsCMOSMEA.McsSegmentStreamEntity

Segment entity class,

class McsPy.McsCMOSMEA.McsTimeStampStream(timestamp_stream_grp)

Container class for one TimeStamp stream

class McsPy.McsCMOSMEA.McsTimeStampStreamEntity

TimeStamp stream entity class

Processed Data (.cmtr) files

class McsPy.McsCMOSMEA.NetworkExplorer(network_explorer_group)

Container class for a NetworkExplorer object

get_sta_entity_by_sourceID(key)

Retrieve the STA Entity for the given source ID.

Parameters:key – A valid source ID. See the sourceIDs attribute for a list of valid source IDs Returns:The STA Entity for the given source ID

get_sta_entity_by_sensorID(key)

Retrieve the STA Entity for the given sensor ID.

Parameters:key – A valid sensor ID. See the sensorIDs attribute for a list of valid sensor IDs Returns:The STA Entity for the given sensor ID

get_sta_entity(key)

Retrieve the STA Entity for the given key.

Parameters:key – A valid key, either a sensor or a source ID, depending on the sta_key_type attribute Returns:The STA Entity for the given key

set_sta_entity(key, value)

Sets an entity to a value

del_sta_entity(key)

Deletes an entity

get_axon_for_entity_by_sourceID(key, axon=1, segment=1)

Retrieve the path of the axon for a given sensor or source ID.

Parameters:

• key – A valid key, either a sensor or a source ID, depending on the sta_key_type attribute
• axon – A valid axon ID, in case multiple axons have been found for a unit. Default: 1
• segment – A valid axon ID, in case multiple segments have been found for an axon. Default: 1

Returns:

The axon path as a list of (X,Y) tuples in sensor coordinates. Returns None if no axon is found

sta_key_type

The type of key used in the access functions. Either ‘sourceID’ or ‘sensorID’

sourceIDs

A list of valid source IDs

sensorIDs

A list of valid sensor IDs

class McsPy.McsCMOSMEA.STAEntity(sta_explorer, sta_entity, spikes_entity=None, stastddev_entity=None, axon=None)

Container Class for a STAEntity object

data

The STA data as a numpy array of shape (frames x sensors_Y x sensor_X)

spikes

Detected spikes in the STA

sta_stddev

Returns the standard deviation for each channel in the STA. Used for spike detection on the STA

sensor_coordinates

Returns the STA source coordinates on the chip as [X,Y]. Note: X and Y are 1-based

axon

Returns the axon path as a list of (X,Y) tuples in sensor coordinates. None if no axon has been found

class McsPy.McsCMOSMEA.SpikeExplorer(spike_explorer_group)

Container Class for an SpikeExplorer object

class McsPy.McsCMOSMEA.SpikeSorter(spike_sorter_group)

Container for SpikeSorter object

get_unit(unit_id)

Retrieves a single unit by its UnitID

Parameters:unit_id – A valid unit ID.

get_units_by_id()

Returns a list of units sorted by unit ID

get_units_by_measure(measure, descending=True)

Returns a list of units ordered by the given quality measure.

Parameters:

• measure – The name of a quality measure. See get_unit_measures() for a list of valid quality measure names.
• descending – The ordering of the list. Default: True (=descending order)

get_unit_measures()

Returns a list of the available unit quality measure names

class McsPy.McsCMOSMEA.SpikeSorterUnitEntity(unit_group)

Container for Spike Sorter Units

get_peaks()

Retrieves all peaks in the source signal where the ‘IncludePeak’ flag is set.

get_peaks_timestamps()

Retrieves the timestamps for all peaks in the source signal where the ‘IncludePeak’ flag is set.

get_peaks_amplitudes()

Retrieves the peak amplitudes for all peaks in the source signal where the ‘IncludePeak’ flag is set.

get_peaks_cutouts()

Retrieves the cutouts for all peaks in the source signal where the ‘IncludePeak’ flag is set.

get_measures()

Gets a list of valid unit quality measures names

get_measure(measure)

Gets a quality measure for this unit

Parameters:measure – The name of a quality measure. See get_measures() for a list of valid quality measure names.

class McsPy.McsCMOSMEA.FilterTool(filter_tool)

Container for FilterTool object

class McsPy.McsCMOSMEA.ActivitySummary(activity_summary)

Container for ActivitySummary object

MCS HDF5 Format Definitions

Definition of the HDF5 format for raw data

MCS-HDF5 Protocol Type: RawData (Raw-Data protocol)

Protocol Version: 3 based on the definitions of RawDataFileIO in version 10.

All strings are only ASCII-encoded

Changelog

Version 1:

• Initial draft

Version 2:

• New Root-Folder attributes added to detect name and version of the creating application and library

Version 3:

• Data structures for DataSubType::Average of StreamType::Segment added

Hierarchy

Root-Folder “/”

Contains all information for one experiment - measured data (inside the folder Data) and a description (possibly in the future) inside the folder Experiment/Description/…

Attributes:

Name	Description	Data Type	MCS-HDF5 Protocol Version
McsHdf5ProtocolType	Type of the used MCS-HDF5 protocol definition (e.g. RawData for the raw data MCS-HDF5 definitions)	[String,Scalar]	1 ≤
McsHdf5ProtocolVersion	Version number of the used MCS-HDF5 protocol	[Integer,Scalar]	1 ≤
GeneratingApplicationName	Name of the application that generated this HDF5 file	[String,Scalar]	2 ≤
GeneratingApplicationVersion	Version of the application that generated this HDF5 file	[String,Scalar]	2 ≤
McsDataToolsVersion	Version of the McsDataTools library that was used by the application to create the HDF5 file	[String,Scalar]	2 ≤

Datasets:

• none

Folder “Data”

Navigation: /Data

Contains all recordings for this experiment.

Attributes:

Name	Description	Data Type
ProgramName	Name of the recording program	[String,Scalar]
ProgramVersion	Version number of the recording program	[String,Scalar]
MeaName	Name of the recorded MEA	[String,Scalar]
MeaLayout	Layout descriptor	[String,Scalar]
MeaSN	Serial number of the MEA	[String,Scalar]
Date	Date of the recording	[String,Scalar]
DateInTicks	Date of the recording in .NET ticks (100 ns)	[Long(64-bit Integer),Scalar]
FileGUID	GUID of the converted raw data file	[String,Scalar]
Comment	Comment	[String,Scalar]

Datasets:

• none

Folder “Recording_x”

Navigation: /Data/Recording_x

Contains all recorded streams for recording x.

Attributes:

Name	Description	Data Type
RecordingID	Recording ID	[Integer(32-bit Integer),Scalar]
RecordingType	Recording type	[String,Scalar]
TimeStamp	Start time of the recording in microseconds	[Long(64-bit Integer),Scalar]
Duration	Total recording duration in microseconds (This duration can differ from the actual duration of the recorded data!!!)	[Long(64-bit Integer),Scalar]
Label	Label	[String,Scalar]
Comment	Comment	[String,Scalar]

Datasets:

• none

Folder “AnalogStream”

Navigation: /Data/Recording_x/AnalogStream

(Organisational) folder for all channel-based streams of this recording

Attributes:

• none

Datasets:

• none

Sub-folder “Stream_x” of “AnalogStream”

Navigation: /Data/Recording_x/AnalogStream/Stream_x

Container for an analog stream

Attributes:

Name	Description	Data Type	StreamInfoVersion
StreamInfoVersion	Version number of the meta information structure	[Int(32-bit Integer),Scalar]	1 ≤
Label	Label	[String,Scalar]	1 ≤
SourceStreamGUID	GUID of the source streams	[String,Scalar]	1 ≤
StreamGUID	GUID	[String,Scalar]	1 ≤
StreamType	Type of the stream, e.g. Electrode	[String,Scalar]	1 ≤
DataSubType	Sub-type of the analog stream (e.g. Analog)	[String,Scalar]	1 ≤

Datasets:

• Matrix InfoChannel → n × 16 matrix of describing information vectors for the n channels:
  • Attributes: InfoVersion → Version number of the Info-Objects [Int(32-bit Integer),Scalar]

Name	Description	Data Type	InfoVersion
ChannelID	ID of the channel as given by the recording software	[Int(32-bit Integer),Array(Size 1)]	1 ≤
RowIndex	Row number of this channel inside the ChannelData matrix where the data of this channel is stored	[Int(32-bit Integer),Array(Size 1)]	1 ≤
GroupID	ID of the group that this channel belongs to	[Int(32-bit Integer),Array(Size 1)]	1 ≤
Label	Label of the channel	[String,Array]	1 ≤
RawDataType	Type of the raw data	[String,Array]	1 ≤
Unit	Physical unit of the measured sensor value	[String,Array]	1 ≤
Exponent	Exponent n ⇒ 1En resp. 10n in which the channel values magnitude is measured (e.g. k,m,µ,…)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
ADZero	ADC-Step that represents the 0-point of the measuring range of the ADC	[Int(32-bit Integer),Array(Size 1)]	1 ≤
Tick	Sample tick Δ between two sample points of a channel in µs ⇒ sampling frequency = 1000000 / Δ	[Long(64-bit Integer),Array(Size 1)]	1 ≤
ConversionFactor	Conversion factor for the mapping ADC-Step ⇒ measured value	[Long(64-bit Integer),Array(Size 1)]	1 ≤
ADCBits	Number of bits used by the AD-Converter	[Int(32-bit Integer),Array(Size 1)]	1 ≤
HighPassFilterType	Type of the high-pass filter (empty string if not available)	[String,Scalar]	1 ≤
HighPassFilterCutOffFrequency	Cut-off frequency of the high-pass filter (‘-1’-String if not available)	[String,Scalar]	1 ≤
HighPassFilterOrder	Order of the high-pass filter (-1 if not available)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
LowPassFilterType	Type of the low-pass filter (empty string if not available)	[String,Scalar]	1 ≤
LowPassFilterCutOffFrequency	Cut-off frequency of the low-pass filter (‘-1’-String if not available)	[String,Scalar]	1 ≤
LowPassFilterOrder	Order of the low-pass filter (-1 if not available)	[Int(32-bit Integer),Array(Size 1)]	1 ≤

• 2-dimensional Data-Matrix ChannelData → Data for sampled channels organized as n × m matrix ⇒ one row per channel and one column per sample time point
  • reconstruct the value of the measured signal: \(y(\text{channel},t_{ind}) = (\text{ChannelData}[\text{InfoChannel}[\text{channel}].\text{RowIndex},t_{ind}] - \text{ADZero}) * \text{InfoChannel}[\text{channel}].\text{ConversionFactor} * 10^{\text{InfoChannel}[\text{channel}].\text{Exponent}}\) in \(\text{InfoChannel}[\text{channel}].\text{Unit}\)
  • reconstruct the sample time point: \(t = t_{ind} * \text{InfoChannel}[\text{channel}].\text{Tick}\) in \(\mu s\)
• Matrix ChannelDataTimeStamps → k × 3 matrix of segments where the rows are one segment and the columns are:
  • first column → time stamp of the first sample point of the segment
  • second column → first index (column) of the segment in ChannelData
  • third column → last index (column) of the segment in ChannelData

Folder “FrameStream”

Navigation: /Data/Recording_x/FrameStream

(Organisational) folder for all frame-based streams of this recording

Attributes:

• none

Datasets:

• none

Subfolder “Stream_x” of “FrameStream”

Navigation: /Data/Recording_x/FrameStream/Stream_x

Folder that contains all Frame-Entities of one Frame-Stream:

Attributes:

Name	Description	Data Type	StreamInfoVersion
StreamInfoVersion	Version number of the meta information structure	[Int(32-bit Integer),Scalar]	1 ≤
Label	Label	[String,Scalar]	1 ≤
SourceStreamGUID	GUID of the source stream	[String,Scalar]	1 ≤
StreamGUID	GUID	[String,Scalar]	1 ≤
StreamType	Type of the stream Frame	[String,Scalar]	1 ≤
DataSubType	Sub-type of the event stream (e.g. SpikeTimeStamp)	[String,Scalar]	1 ≤

Datasets:

• Matrix InfoFrame → n × 24 matrix of describing information vectors for the n Frame-Entities:
  • Attributes: InfoVersion → Version number of the Info-Objects [Int(32-bit Integer),Scalar]

Name	Description	Data Type	InfoVersion
FrameID	ID of the frame entity as given by the recording software	[Int(32-bit Integer),Array(Size 1)]	1 ≤
FrameDataID	ID of the frame entity inside the stream folder that maps this information vector to the entity folder (FrameDataID → subfolder FrameDataEntity_FrameDataID)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
GroupID	ID of the group that this frame entity belongs to	[Int(32-bit Integer),Array(Size 1)]	1 ≤
Label	Label of the entity	[String,Array]	1 ≤
RawDataType	Type of the raw data	[String,Array]	1 ≤
Unit	Physical unit of the measured sensor value	[String,Array]	1 ≤
Exponent	Exponent n ⇒ 1En resp. 10n in which the sensor values magnitude is measured (e.g. k,m,µ,…)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
ADZero	ADC-Step that represents the 0-point of the measuring range of the ADC	[Int(32-bit Integer),Array(Size 1)]	1 ≤
ADCBits	Number of bits used by the AD-Converter	[Int(32-bit Integer),Array(Size 1)]	1 ≤
Tick	Sample tick Δ between two frames in µs ⇒ sampling frequency = 1000000 / Δ	[Long(64-bit Integer),Array(Size 1)]	1 ≤
HighPassFilterType	Type of the high-pass filter (empty string if not available)	[String,Scalar]	1 ≤
HighPassFilterCutOffFrequency	Cut-off frequency of the high-pass filter (‘-1’-String if not available)	[String,Scalar]	1 ≤
HighPassFilterOrder	Order of the high-pass filter (-1 if not available)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
LowPassFilterType	Type of the low-pass filter (empty string if not available)	[String,Scalar]	1 ≤
LowPassFilterCutOffFrequency	Cut-off frequency of the low-pass filter (‘-1’-String if not available)	[String,Scalar]	1 ≤
LowPassFilterOrder	Order of the low-pass filter (-1 if not available)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
SensorSpacing	Distance between adjacent sensors in µm	[Int(32-bit Integer),Array(Size 1)]	1 ≤
FrameLeft	Sensor count of the left edge of the entity frame based on the reference frame	[Int(32-bit Integer),Array(Size 1)]	1 ≤
FrameTop	Sensor count of the top edge of the entity frame based on the reference frame	[Int(32-bit Integer),Array(Size 1)]	1 ≤
FrameRight	Sensor count of the right edge of the entity frame based on the reference frame	[Int(32-bit Integer),Array(Size 1)]	1 ≤
FrameBottom	Sensor count of the bottom edge of the entity frame based on the reference frame	[Int(32-bit Integer),Array(Size 1)]	1 ≤
ReferenceFrameLeft	Sensor count of the left edge of the reference frame (defined by the used sensor array)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
ReferenceFrameTop	Sensor count of the left edge of the reference frame (defined by the used sensor array)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
ReferenceFrameRight	Sensor count of the left edge of the reference frame (defined by the used sensor array)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
ReferenceFrameBottom	Sensor count of the left edge of the reference frame (defined by the used sensor array)	[Int(32-bit Integer),Array(Size 1)]	1 ≤

Subfolder “FrameDataEntity_x”

Navigation: /Data/Recording_x/FrameStream/Stream_x/FrameDataEntity_x

Contains all datasets of the Frame-Entity x

Datasets:

• Matrix ConversionFactors → n × m matrix of conversion factors for the sensor array
• 3-dimensional Data-Cube FrameData → cube of the frame data organized as one frame to one sample time point (n × m matrix of sampled signal values per sensor) × sample time points
  • reconstruct the value of the measured signal: y = (FrameData[x,y,t] - ADZero) * ConversionFactors[x,y]
  • reconstruct the sample time point:
• Matrix FrameDataTimeStamps → k × 3 matrix of segments where the rows are one segment and the columns are:
  • first column → time stamp of the first sample point of the segment
  • second column → first index (z-axis) of the segment in FrameData
  • third column → last index (z-axis) of the segment in FrameData

Datasets:

• none

Folder “EventStream”

Navigation: /Data/Recording_x/EventStream

(Organisational) folder for all event-based streams of this recording

Attributes:

• none

Datasets:

• none

Subfolder “Stream_x” of “EventStream”

Navigation: /Data/Recording_x/EventStream/Stream_x

Folder that contains all Event-Entities of one Event-Stream:

Attributes:

Name	Description	Data Type	StreamInfoVersion
StreamInfoVersion	Version number of the meta information structure	[Int(32-bit Integer),Scalar]	1 ≤
Label	Label	[String,Scalar]	1 ≤
SourceStreamGUID	GUID of the source stream	[String,Scalar]	1 ≤
StreamGUID	GUID of the current stream	[String,Scalar]	1 ≤
StreamType	Type of the stream Event	[String,Scalar]	1 ≤
DataSubType	Sub-type of the event stream (e.g. StgSideband, UserInput, DigitalPort)	[String,Scalar]	1 ≤

Sub-type Description:

• StgSideband → The event is associated to a STG sideband change.
• UserInput → The event is associated with an user input.
• DigitalPort → The event is associated with a digital port change.

Datasets:

• Matrix InfoEvent → n × 7 matrix of describing information vectors for the n Event-Entities:
  • Attributes: InfoVersion → Version number of the Info-Objects [Int(32-bit Integer),Scalar]

Name	Description	Data Type	InfoVersion
EventID	ID of the event entity	[Int(32-bit Integer),Array(Size 1)]	1 ≤
GroupID	ID of the group that the entity belongs to	[Int(32-bit Integer),Array(Size 1)]	1 ≤
Label	Label of the entity	[String,Array]	1 ≤
RawDataType	Type of the raw data	[String,Array]	1 ≤
RawDataBytes	Number of bytes of the raw data type	[Int(32-bit Integer),Array(Size 1)]	1 ≤
SourceChannelIDs	Comma separated list of ID’s of (source) channel that were involved in the generation of this event	[String,Array]	1 ≤
SourceChannelLabels	Comma separated list of labels of the source channels	[String,Scalar]	1 ≤

• 2-dimensional matrix EventEntity_x → 2 × n matrix ⇒ n events with describing vector (time stamp of event, duration of event)
  • Attributes: Short description of content
  • \(t_\text{event i} = \text{EventEntity}\_x[0,i]\) in \(\mu s\)
  • \(\Delta_\text{event i} = \text{EventEntity}\_x[1,i]\) in \(\mu s\)

Folder “SegmentStream”

Navigation: /Data/Recording_x/SegmentStream

(Organisational) folder for all segment-based streams of this recording. A segment is a cutout of parts of the sampled signal relative to an event, defined by a pre- and post interval.

Attributes:

• none

Datasets:

• none

Subfolder “Stream_x” of “SegmentStream”

Navigation: /Data/Recording_x/SegmentStream/Stream_x

Folder that contains all Segment-Entities of one Segment-Stream:

Attributes:

Name	Description	Data Type	StreamInfoVersion
StreamInfoVersion	Version number of the meta information structure	[Int(32-bit Integer),Scalar]	1 ≤
Label	Label	[String,Scalar]	1 ≤
SourceStreamGUID	GUID of the source stream	[String,Scalar]	1 ≤
StreamGUID	GUID of the current stream	[String,Scalar]	1 ≤
StreamType	Type of the stream Segment	[String,Scalar]	1 ≤
DataSubType	Sub-type of the segment stream (e.g. Spike)	[String,Scalar]	1 ≤

Datasets:

• Matrix InfoSegment → n × 7 matrix of describing information vectors for the n Segment-Entities:
  • Attributes: InfoVersion → Version number of the Info-Objects [Int(32-bit Integer),Scalar]

Name	Description	Data Type	InfoVersion
SegmentID	ID of the segment entity	[Int(32-bit Integer),Array(Size 1)]	1 ≤
GroupID	ID of the group that the segment entity belongs to	[Int(32-bit Integer),Array(Size 1)]	1 ≤
Label	Label of the entity	[String,Array]	1 ≤
PreInterval	Time interval in µs before the segment defining event occured - definition of the beginning of the segment	[Int(64-bit Integer),Array(Size 1)]	1 ≤
PostInterval	Time interval in µs after the segment defining event occured - definition of the end of the segment \(\text{length of the segment} = \text{PreInterval} + \text{PostInterval}\) in µs	[Int(64-bit Integer),Array(Size 1)]	1 ≤
SegmentType	Type of the segment (e.g. SpikeCutout)	[String,Array]	1 ≤
SourceChannelIDs	Comma separated list of ID’s of (source) channels that the segements are taken from → Link to the SourceChannelInfo matrix	[String,Array]	1 ≤

• 2-dimensional matrix SourceChannelInfo → n × 15 matrix ⇒ n of describing vectors for the n source channels, the structure is the same as in ChannelInfo used in section Sub-folder “Stream_x” of “AnalogStream”
  • Attributes: InfoVersion → Version number of the Info-Objects [Int(32-bit Integer),Scalar]
• Vector SegmentData_ts_x → n time stamps in µs of the event triggering the segment, one for each of the n segments contained by segment entity x
• 2-dimensional matrix or 3-dimensional cube SegmentData_x → k × n matrix (k sample points for one segment, n number of sampled segments) or k × m × n cube (k sample points for one segment, m number of segments for one time point/for one multi-segment, n number of sampled multi-segments) of segment data:
  • Attributes: SourceChannelID → Comma separated list of ID’s of (source) channels that the segements are taken from [String,Scalar] (the same as in InfoSegment, repeated for clarification)
  • reconstruct the value of the measured segment signal (only one segment \(id_\text{segment}\) → 2-dimensional matrix M[row,col]):
    • \(t_{ind}[row,col] = \text{SegmentData ts x}[\text{col}] + (\text{row} - 1) * \text{tick}_\text{source-channel} - \text{PreInterval}\) in µs
    • \(y(id_\text{segment},t_{ind}(row,col)) = (\text{SegmentData x}[row, col] - \text{ADZero}_\text{source-channel}) * \text{ConversionFactor}_\text{source-channel} * 10^{\text{Exponent}_\text{source-channel}}\) in \(\text{InfoChannel}[\text{source-channel}].\text{Unit}\)
  • reconstruct the value of the measured segment signal (m segments → multi-segments → 3-dimensional cube M[row,col,z]):
    • col → \(id_\text{segment}\) → source-channel
    • \(t_{ind}[row,col,z] = \text{SegmentData ts x}[\text{z}] + (\text{row} - 1) * \text{tick}_{\text{source-channel}[col]}\) in µs
    • \(y(id_\text{segment},t_{ind}(row, z)) = (\text{SegmentData x}[row, \text{col}, z] - \text{ADZero}_{\text{source-channel}[\text{col}]}) * \text{ConversionFactor}_{\text{source-channel}[\text{col}]} * 10^{\text{Exponent}_{\text{source-channel}[\text{col}]}}\) in \(\text{InfoChannel}[\text{source-channel}[\text{col}]].\text{Unit}\)

DataSubType-Average: Subfolder “Stream_x” of “SegmentStream”

Navigation: /Data/Recording_x/SegmentStream/Stream_x

Folder that contains all Segment-Entities of one Segment-Stream with DataSybType == Average:

Attributes: no difference to the standard case above

Datasets:

• Matrix InfoSegment: no difference to the standard case above
• Matrix SourceChannelInfo: no difference to the standard case above
• (3 × n) matrix AverageData_Range_x → (start, end, count) per segment average × count of segment averages contained by segment entity x. start and end denote the start and end timestamp in µs of the interval that contains all averaged segments. count is the number of averaged segments.
  • Attributes: description of the content
• (2 × k × n) cube AverageData_x → (mean and standard deviation) × k sample points of the segment × n number of segment averages
  • Attributes:: description of the content
  • reconstruct the value of the mean and standard deviation of the average segment (n average segments → 3-dimensional cube M[row,col,z]):
    • row: mean → row = 0; StdDev → row = 1
    • col: \(t_{ind}(col) = (\text{col} - 1) * \text{tick}_{\text{source-channel}}\) → time range \((0, PreInterval[\text{SegmentID}] + PreInterval[\text{SegmentID}])\) in µs
    • z: z = \(id_\text{average}\) (number of average segment)
    • \(Mean(id_\text{average},t_{ind}(col)) = (\text{AverageData x}[0, \text{col}, id_\text{average}] - \text{ADZero}_{\text{source-channel}}) * \text{ConversionFactor}_{\text{source-channel}} * 10^{\text{Exponent}_{\text{source-channel}}}\) in \(\text{InfoChannel}_\text{source-channel}.\text{Unit}\)
    • \(StdDev(id_\text{average},t_{ind}(col)) = \text{AverageData x}[1, \text{col}, id_\text{average}] * \text{ConversionFactor}_{\text{source-channel}} * 10^{\text{Exponent}_{\text{source-channel}}}\) in \(\text{InfoChannel}_\text{source-channel}.\text{Unit}\)

Folder “TimeStampStream”

Navigation: /Data/Recording_x/TimeStampStream

(Organisational) folder for all TimeStamp-based streams of this recording

Attributes:

• none

Datasets:

• none

Subfolder “Stream_x” of “TimeStampStream”

Navigation: /Data/Recording_x/TimeStampStream/Stream_x

Folder that contains all TimeStamp-Entities of one TimeStamp-Stream:

Attributes:

Name	Description	Data Type	StreamInfoVersion
StreamInfoVersion	Version number of the meta information structure	[Int(32-bit Integer),Scalar]	1 ≤
Label	Label	[String,Scalar]	1 ≤
SourceStreamGUID	GUID of the source stream	[String,Scalar]	1 ≤
StreamGUID	GUID of the current stream	[String,Scalar]	1 ≤
StreamType	Type of the stream TimeStamp	[String,Scalar]	1 ≤
DataSubType	Sub-type of the TimeStamp stream (e.g. NeuralSpike)	[String,Scalar]	1 ≤

Sub-type Description:

• NeuralSpike → The entity contains time stamps of neural spikes

Datasets:

• Matrix InfoTimeStamp → n × 7 matrix of describing information vectors for the n Event-Entities:
  • Attributes: InfoVersion → Version number of the Info-Objects [Int(32-bit Integer),Scalar]

Name	Description	Data Type	InfoVersion
TimeStampEntityID	ID of the event entity	[Int(32-bit Integer),Array(Size 1)]	1 ≤
GroupID	ID of the group that the entity belongs to	[Int(32-bit Integer),Array(Size 1)]	1 ≤
Label	Label of the entity	[String,Array]	1 ≤
Unit	Physical unit of the measured sensor value	[String,Array]	1 ≤
Exponent	Exponent n ⇒ 1En resp. 10n in which the channel values magnitude is measured (e.g. k,m,µ,…)	[Int(32-bit Integer),Array(Size 1)]	1 ≤
SourceChannelIDs	Comma separated list of ID’s of (source) channel that were involved in the generation of this event	[String,Array]	1 ≤
SourceChannelLabels	Comma separated list of labels of the source channels	[String,Scalar]	1 ≤

• Vector TimeStampEntity_x → n time stamps in \(\mu s\)

Comment

All time-related information except dates (100ns ticks) are given in \(\mu s\) ticks!!

Category:Software

McsPyDataTools Tutorials

These tutorials can be found as Jupyter Notebooks in the McsPyDataNotebooks folder of the Github repository. They rely on data files that are distributed either in the McsPy/tests/TestData folder or (in case of larger test files) as part of a separate repository that can be downloaded from https://download.multichannelsystems.com/download_data/software/multi-channel-datamanager/McsPyDataTools-TestDataFiles.zip

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“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”: {

“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(“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”: {

“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(“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”: {

“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=”1201”>Loading BokehJS …</span>n”, ” </div>”

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

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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));”

], “application/vnd.bokehjs_load.v0+json”: “n(function(root) {n function now() {n return new Date();n }nn var force = true;nn if (typeof root._bokeh_onload_callbacks === “undefined” || force === true) {n root._bokeh_onload_callbacks = [];n root._bokeh_is_loading = undefined;n }nn nn n if (typeof (root._bokeh_timeout) === “undefined” || force === true) {n root._bokeh_timeout = Date.now() + 5000;n root._bokeh_failed_load = false;n }nn 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>”}};nn 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 }nnn 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 }nn function load_libs(css_urls, js_urls, callback) {n if (css_urls == null) css_urls = [];n if (js_urls == null) js_urls = [];nn 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;nn 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 }nn function on_error() {n console.error(“failed to load ” + url);n }nn 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 }nn 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 }nn function inject_raw_css(css) {n const element = document.createElement(“style”);n element.appendChild(document.createTextNode(css));n document.body.appendChild(element);n }nn 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 nn var inline_js = [n function(Bokeh) {n Bokeh.set_log_level(“info”);n },n function(Bokeh) {n n n }n ];nn 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 }nn }nn 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”: {

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“n”, “n”, “n”, “n”, “n”, “n”, ” <div class=”bk-root” id=”1bed027a-6195-4f4d-905b-1a2aa56394a6” data-root-id=”1202”></div>n”

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}

], “source”: [

“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”: [

“[-0.09765632 -0.28266927 -0.0495911 -0.05989079 -0.01106263 -0.00648499n”, ” -0.03776553 -0.33645654 -0.04348758 -0.05645756 -0.00915528 -0.00610352n”, ” -0.01182557 -0.00076294 -0.00801087 -0.0038147 -0.03395083 -0.3089907n”, ” -0.03623965 -0.06179814 -0.28305074 0.01716615 -0.02403261 -0.05836491n”, ” -0.01373292 0.00152588 -0.21858231 -0.12359628 -0.00839234 -0.05836491n”, ” -0.11138924 -0.26206989 -0.05264286 -0.05950932 -0.00839234 0.00267029n”, ” -0.05378727 -0.33035302 -0.05378727 -0.05455021 -0.00915528 0.00267029n”, ” -0.03547671 -0.33226037 -0.04920963 -0.05416874 -0.02593996 -0.30212424n”, ” -0.03242495 -0.0648499 -0.00839234 0.01106263]n”, “voltn”

]

}, {

“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=”1318”>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(“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”, “n”, “n”, “n”, “n”, “n”, ” <div class=”bk-root” id=”d30bd158-7d4b-4f60-b181-dc283cdae9ac” data-root-id=”1319”></div>n”

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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”: [

{

“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=”5285”>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(“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));”

], “application/vnd.bokehjs_load.v0+json”: “n(function(root) {n function now() {n return new Date();n }nn var force = true;nn if (typeof root._bokeh_onload_callbacks === “undefined” || force === true) {n root._bokeh_onload_callbacks = [];n root._bokeh_is_loading = undefined;n }nn nn n if (typeof (root._bokeh_timeout) === “undefined” || force === true) {n root._bokeh_timeout = Date.now() + 5000;n root._bokeh_failed_load = false;n }nn 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>”}};nn 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 }nnn 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 }nn function load_libs(css_urls, js_urls, callback) {n if (css_urls == null) css_urls = [];n if (js_urls == null) js_urls = [];nn 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;nn 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 }nn function on_error() {n console.error(“failed to load ” + url);n }nn 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 }nn 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 }nn function inject_raw_css(css) {n const element = document.createElement(“style”);n element.appendChild(document.createTextNode(css));n document.body.appendChild(element);n }nn 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 nn var inline_js = [n function(Bokeh) {n Bokeh.set_log_level(“info”);n },n function(Bokeh) {n n n }n ];nn 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 }nn }nn 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”:”4c8ef554-1cd5-4e23-a984-c50095a5ea0e”,”roots”:{“6748”:”efc73d14-283e-46c3-883f-c9adba9e8e0c”}}];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”: {

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“id”: “6748”

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}, “output_type”: “display_data”

}

], “source”: [

“bokeh.io.output_notebook() # see comment for bokeh module in “Requirements” sectionn”, “bfig = bokeh.plotting.figure(plot_width=900, plot_height=400, title=’All Treatments’)n”, “for sheet in range(9):n”, ” bfig.line(# plot line with it’s own colorn”, ” list(range(average_data_31.shape[1])), n”, ” average_data_31[0,:5100,sheet],n”, ” color = Spectral11[sheet], n”, ” legend_label = “Data_”+str(sheet),n”, ” line_width = 2,n”, ” alpha = 0.8n”, ” )n”, “bfig.legend.location = “top_right”n”, “bfig.legend.click_policy =”hide”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)”

]

}, {

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

“Plotting the data from all sensors might look like this”

]

}, {

“cell_type”: “code”, “execution_count”: 85, “metadata”: {}, “outputs”: [

{

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 1080x720 with 12 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“# index_list: see aboven”, “n”, “fig, ((ax1, ax2, ax3, ax4), (ax5, ax6, ax7, ax8), (ax9, ax10, ax11, ax12)) = plt.subplots(3, 4, sharex=’col’, sharey=’row’)n”, “fig.set_size_inches(6,6)n”, “ax_list = [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9, ax10, ax11, ax12]n”, “n”, “fig.set_size_inches(15,10)n”, “n”, “for i in range(len(ax_list)-1):n”, ” x = average_raw_data.recordings[0].segment_streams[0].segment_entity[id_list[i]].data.shape[2]n”, ” color = iter(plt.cm.jet(np.linspace(0,1,x))) # generate as many distinct colors as there are lines that will be plottedn”, ” # you can replace jet with cool or winter or any other matplotlib colormapn”, ” for k in range(x):n”, ” current_average = average_raw_data.recordings[0].segment_streams[0].segment_entity[id_list[i]].data[0,:5100,k]n”, ” c = next(color) # select color from custom colormapn”, ” ax_list[i].plot(current_average, c = c)n”, ” ax_list[i].set_title(“Index: “+str(id_list[i]))n”, “plt.show()”

]

}, {

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

“<a href=’#Top’>Back to index</a>”

]

}, {

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

“### TimestampStream <a id=’TS’></a>n”, “n”, “As you might have noticed some of the plots already accessed the according timestamps. Depending on your settings these may stand for beginnings and/or ends of certain events.n”, “n”, “Accessing the data within a TimestampStream is achieved by calling the .get_timestamps() function of a .timestamp_entity within .timestamp_streams:”

]

}, {

“cell_type”: “code”, “execution_count”: 86, “metadata”: {}, “outputs”: [], “source”: [

“timestamps_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”: 87, “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/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”, “n”, “(array([[ 944000, 954000, 964000, 3030000, 3040000, 3052000,n”, ” 3096000, 5104000, 5116000, 5126000, 7204000, 7212000,n”, ” 7226000, 9290000, 9298000, 11376000, 11386000, 11442000,n”, ” 13462000, 13472000, 13528000, 15548000, 15558000, 17634000,n”, ” 17644000, 17686000]], dtype=int64), <Quantity(1e-06, ‘second’)>)n”, “n”, “Timestamps: [ 944000 954000 964000 3030000 3040000 3052000 3096000 5104000n”, ” 5116000 5126000 7204000 7212000 7226000 9290000 9298000 11376000n”, ” 11386000 11442000 13462000 13472000 13528000 15548000 15558000 17634000n”, ” 17644000 17686000]n”, “n”, “Unit: 1e-06 secondn”

]

}

], “source”: [

“timestamps = timestamps_raw_data.recordings[0].timestamp_streams[0].timestamp_entity[0].get_timestamps()n”, “print()n”, “n”, “# Array of everything concerning the timestamps of the entityn”, “print(timestamps)n”, “print()n”, “n”, “# Just the timestampsn”, “print(“Timestamps: “, timestamps[0][0])n”, “print()n”, “n”, “# The unit the values are inn”, “print(“Unit: “, timestamps[1])”

]

}, {

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

“To illustrate how this data might be used for visualization purposes, we can plot these together with some data from the AnalogStream.n”, “n”, “We will use the built-in functions .get_channel_in_range() and .get_channel_sample_timestamps()”

]

}, {

“cell_type”: “code”, “execution_count”: 88, “metadata”: {}, “outputs”: [

{

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

“[[ 944000 954000 964000 3030000 3040000 3052000 3096000 5104000n”, ” 5116000 5126000 7204000 7212000 7226000 9290000 9298000 11376000n”, ” 11386000 11442000 13462000 13472000 13528000 15548000 15558000 17634000n”, ” 17644000 17686000]]n”, “Time1 (array([ 0, 2000, 4000, …, 5996000, 5998000, 6000000],n”, ” dtype=int64), <Unit(‘microsecond’)>)n”, “Time2 (array([ 100000, 102000, 104000, …, 6096000, 6098000, 6100000],n”, ” dtype=int64), <Unit(‘microsecond’)>)n”

]

}, {

“data”: {

“text/plain”: [

“Text(0.5, 1.0, “Sampled signal overlay ‘Filter (1) Filter Data’ and ‘Data Acquisition (1) Electrode Raw Data’”)”

]

}, “execution_count”: 88, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

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rkDCzU+jlAGFhk9k7gCvDfGFvugwO1VtZ2g8XA8vwBV/zw397AOuA853vNvAA8Pswb8kxs1TB2K3AJWa2t/kWHV3zjdeAs8L9MotuRiMP88ELzWxU+LtP5XOZdP0ttgub5T4D/Cj8Tc4L19nfQq/76dKUNTz/UudcXvg+3TH4/TVQPeUf9wITzOy/zA8uWGJmh4bfbQamWw8jN/diP6VfS6YAn0+b/QWgzsy+bn4QrKj5x6z1aiRw57tFbAA+FX60o9/q48Bx+HEQ1uG7I52MH5fi1d6s0/yAlZ/D/z6/GTaPTvXh3hpOcykdBebg9+MU84MMpvR0HoqMKAp8RYameuBQ4Hkza8QHvG/iazDB9zc9AKjFD9ByxyCs83F80+NH8E24Huo6QXhjcQa+CdhWfCn5V+nISy4I012Fvxj/rYf1zcYHQA34QbF+7zI/u/cq/M3qynD6f9L7xzr9EijAD2rzHP6RHb31A3wgvxB4Az84WPvowOGN87PA4aQVEvRiH+1Ib47tX/GDkfSlmXOfOeea8P2inw6bzR3mnLsT+AlwS9iE7018X8m++BOd+yfuhi+gSI3q3AwsTfv+dGCBcy5TK4SB+BG+cKPGzK4YhGOXco+Z1Yfz/z98/75L076/HLgqnOa7pNUCZtrn9PN8D5t8fiFcfjX+/Ly7D9vxOXyz5034Fid/6cO8kN398FF8nrIp/R8+8EoF8Bfj++C+ha8x+69w3Q/g845H8Xnio12W/QugDR9E/JWeA9CLgVXhufJpOropdNXpt5jh+/PxTWo34Mdo+F4YbPXH/wEXhoWOKc34vBj8/mjvnx8Gf43OP9aoO5Ns++f4bvd4pJ7yj/D3eiK+wHQTvnXIceGst4WvlWbWUz/pnvbT9/HNm1fi+323559hQc9p+AKSlfhrxp+BUT2sq6v/Ab4WFtT1+Ft1zi3D7+8nw/d1+MG0nnY7fnZzTXhf8Aa+Gf05zrnrwuUsBn6OvzZtxl8nnk6b91F8PrvJOsZ56PY8FBlpUiOoisi7lJlNx1/oc7r09RuSzOwzwHnOuXft4BvmBxJ7C5gQ3jANO+Yf//J559wOazfM7Hng484PsCSyy5mZA2andWkY1szsZnz/57t6Me3twLXOD34mIjJsKfAVeZcb6oGv+dFXZ+JLsGfjS89/65wb0LNrh6uwqd//AqXOuY9lOz0i7wYjLfAVEXk3yjTin4jIUJKLbxo7Az+q6C345zS+65gfOGwzvrneyVlOjoiIiMiwoRpfERERERERGdE0uJWIiIiIiIiMaAp8RUREREREZER7V/XxraiocNOnT892MkQ6LF0Kzc1QUNDx2Z57Zi89IjL87ShfWbp0+89ERDLRfYoMQy+//PI259zYrp+/qwLf6dOn89JLL2U7GSIdjj0WXnsN5s/v+GzBgmylRkRGgh3lK8ceu/1nIiKZ6D5FhiEzW53pczV1FhERERERkRFNga+IiIiIiIiMaAp8RUREREREZERT4CsiIiIiIiIjmgJfERERERERGdEU+IqIiIiIiMiIpsBXRERERERERjQFviIiIiIiIjKiKfAVERERERGREU2Br4iIiIiIiIxoCnxFRERERERkRFPgKyIiMhI5B/HmbKdCRERkSFDgKyIiMhJVr4L1L0OiNdspERERyToFviIiIiNRc5V/TbZlNx0iIiJDgAJfERGRkcg5/6rAV0RERIGvyJDgHNSugyCZ7ZSIyEhh5l+T8eymQ0REZAiIZTsBIgK01Pj+ePGmbKdEREYaFaiJyKBxEAQQUd2ZDD/61YoMJW2N2U6BiIwUqabOLpHddIjIyLHmebj14mynQqRfFPiKDAWpGhkXZDcdIjJyuDBfUY2viAyGIAlBAt66N9spEekXBb4iQ4FT4CsigyyVnwSq8RWRQaCB8mSYU+ArMhQEqpkRkUGWauqsfEVEBoNLy0vUNUuGIQW+IkOBanxFZNClAl/V+IrIIEgvRGvcmr10iPSTAl+RLIsnHUEyLfBNqCmRiAwC1fiKyGBKr/Ft3Ja9dIj0kwJfkSxqSQQ0xxPUNLZ0fNiki4mIDCKN6iwig0E1vjLMKfAVySIX1sg4XUxEZGdRja+IDIb07li6V5FhSIGvSBZZ+BpBFxMRGUyu408FviIyGBT4yjCnwFckm8yHvhECiMT8Z+o3IyKDJZoTjh3Qmu2UiMhw59IK1HSvIsOQAl+RLGqv8XUBxPL8G5WiishApe5PUwVqrfVZS4qIjBCpGt+icbpXkWFJga9IFoUVvhjO36BaRBcTERk8kRz/2lKb3XSIyLBW1dhGY2vcvymZAE2V2U2QSD8o8BUZEpyPgiMxaNTFREQGKqzyjarGV0QGbtnmeqobwy4TheXKU2RYUuArMmQYRKIQb8x2QkRkpEjV+LbWZTcdIjLsWaqQPq8EWhuynRyRPlPgK5JNYaWMtf8RhTYFviIySFJ9fFsU+IrIwERwvktWbgm0KfCV4UeBr0gWufQ3Ftb4tjVlKzkiMgLEk45kkGrqnKrxVbNEERkYX0hvkFesPEWGpawGvmZ2spktNbN3zOwbGb7/spktNrOFZvaIme2W9l3SzF4L/929a1MuMrjaLyYWUSmqiAzI21vqaY6Hz+5tr/HV4FYiMjDWXuNb7O9VnNvxTCJDSCxbKzazKPA74ERgHfCimd3tnFucNtmrwEHOuSYz+wzwU+Dc8Ltm59z8XZpokZ0tEoW4anxFpP/aEgEdg1uFNb4aLV5EBijS3se3GIIEJFogpyDbyRLptWzW+B4CvOOcW+GcawNuAc5In8A595hzLhUFPAdM2cVpFNkl2geMiEShuSbbyRGRYSwasfZnhGMRH/w2bslmkkRkBGi/V8kt8R9ogCsZZrIZ+E4G1qa9Xxd+1p2PAw+kvc83s5fM7Dkz++DOSKDIrmUQzfM3qInWbCdGREYEg2guNKjGV0QGxlL/54WBb5v6+crwkrWmztBRIJ0mY2cBM7sIOAg4Ju3jac65DWY2E3jUzN5wzi3PMO+ngE8BTJs2beCpFtkJ2kd1juX517oNUD4jewkSkZHBTDW+IjJI0ga3AtX4yrCTzRrfdcDUtPdTgA1dJzKzE4D/B5zunGuvBnPObQhfVwALgP0zrcQ593/OuYOccweNHTt28FIvMtjMOp652aLmziLSP9b1nWp8RWQQWOq/VI2vRnaWYSabge+LwGwzm2FmucB5QKfRmc1sf+BP+KB3S9rno80sL/y7AjgCSB8US2RYSDVxaG8+FIn6D9TPV0T6y9KfDR4WqKnGV0QGhUFhhf9T+YoMM1lr6uycS5jZ54AHgShwnXNukZldBbzknLsb+B+gGLjNzADWOOdOB+YAfzKzAB+8/7jLaNAiw0r7TaoePSIiA5ZW52sG0ZgffTXRBrHc7CVLRIa19kcvlkzwHzQo8JXhJZt9fHHO3Q/c3+Wz76b9fUI38z0D7LtzUyeyi5kp8BWRAduuqbOF+UprHcQqspAiERkxDCgo9/crDZuznRqRPslmU2cRSbVGVI2viOwMltaFQvmKiAxAe41vJAL5o9QtS4YdBb4iWZU+kHl4g2pR3aCKyCBRSxIRGUxhm5L8Mg3EKcOOAl+RIaD9ofDgS1F1MRGRAWhv7pxe49tal63kiMgI4O9Vwjf5o1SYJsOOAl+RIcDS/tfFREQGLm3M+PYaXwW+ItJ/1v48I6CgTE2dZdhR4CuSRS7Thwp8RWQAzLq8UY2viAw2NXWWYUiBr8gQsH1TZwW+IjIY1MdXRAZHp3sV1fjKMKTAV2SoKSjTDaqIDMj2fXxNN6kiMiDbd8uqAZex7ZrIkKTAV2QI2K7GVzeoItJP2z3HF4OisXrmpogMUFqQm18GQQLiTdlLjkgfKfAVGQI6laIWjYWmbZBMZDFFIjIipArUSsYr8BWRAfFjW6U1dQYV1MuwosBXZKgp392XotasznZKRGTY6tL8sHgC1G/MTlJEZIRwdHqOL2iAKxlWFPiKDBWpUtSyaf61bn320iIiw1fnYZ39S8kEqFeNr4j0n5FWpJY/yr+qxleGEQW+IkNGeINaPM6/NmzJXlJEZGRIxcAlE6BxCwTJrCZHRIa7Lk2dVeMrw4gCX5EsyjgYYnvgq9oZEem77Qe3AorGgQugqSoLKRKRkcAPxBm+aW/qrOeDy/ChwFdkqLC0fjPRXAW+ItJv1vWDogr/2rRtVydFREaUtCdQgB6/KMOKAl+RISO8mJj52hk1dRaRAejUoKRorH9t3JqNpIjICGDpuUpeqX9V4CvDiAJfkSzbrmYGfHNn1fiKSD9Yhr8U+IrIQPnBrcJ8JRqD3BJoVvcJGT4U+IoMFekjsRaPhwbdoIrIIEk1dW5UU2cR6S/X+V5l7J6w4bXsJUekjxT4imRdhhGuVOMrIv1lXV4BCkaDRVTjKyJ9lrpL2a6F2qT5sG3pLk6NSP8p8BUZMrrU+DZt06NHRGQA0vKUSBRGT4fNi7KWGhEZ7hyd8pWyadBcrZGdZdhQ4CuSRZ3qejs1dQ4fPaJmiSLST9u1JZl8EGx6IxtJEZERwPfxTTNqqn+tXZuF1Ij0nQJfkSGjS40vqLmziPRZxsGtAMqmQt2Gbh4gLiLSR2W7+deaNdlNh0gvKfAVGYpSgW+jHmkkIoOkdDK4JATxbKdEREaCsmn+VYGvDBMKfEWyynXUyXRt6gx6lq+I9EPGh6RB4Rj/mlTgKyJ9t13OUlQBsQIFvjJsKPAVyabuWhy2B75q6iwifWddB6EBKCz3r0Fil6dHREYCl15c7wvsSyf5LhQiw4ACX5EhI+1iklsEhRWwZUn2kiMiw1Kq8ch25WoFo/1rvGlXJkdERrK8EmhryHYqRHpFga/IUGFdamemHwFrX8hOWkRk5KnYE4onQFNltlMiIiNFXgm0KvCV4UGBr0jWddPeecxs329G/fFEpD+6FqbFcmH2idDWmJ30iMjIk1sEbfXZToVIryjwFcmiziFvl5vUMbv7EVg1aISI9EE3Q1t5ZdN8YZoLdlVyRGSE2O45vgCb3vTPB29USxIZ+hT4imRZxlGdAcpn+tfFd+3K5IjICOEyhcClk/xronXXJkZERogu+UpqEM6Nr+36pIj0kQJfkaFq0v7+eb6L7852SkRkpJhyiH9tqcluOkRkGMrQNevS+/2rxiSRYUCBr8iQ0bU/Xh7sczZsWwauu+ceiYh00VNb54rZEMuHpqpdlhwRGRkyZi1j9/Svj/8YEm27MjkifabAV2So6NrUGaBsqn/0iEZhFZHeai8ny5CnmEHhaGip3ZUpEpERYrti+PxRMO09/u9VT+zq5Ij0iQJfkaGsbJp/1QBXItIHGQehSYnk+MGtAg1wJSI9aKmDxq07nu7MP/rX+k07Nz0iA6TAVySLehzVGWDUVP+qwFdEdmTdS7278bTw0h9v2rnpEZHh7fGfwNal0FpHCT3kF8UT/Gv9xl2TLpF+imU7ASLvSutfhtdv6dx3N1NT55KJ/rVhy65Jl4gMT87Bn48HYGxbfs/TRqL+Nd4EecU7OWEiMmyFjz0z55gbWU2AZW5JkpMPuSXQVL1LkyfSVwp8RbLhX5+HLYso2dRIvKeRaPJK/KseDr9rBQE0V0FRhX/vHGx7GxItMHFedtPWH0EAkQjUbYTHfgBlu8G+50D5jGynTAZLWuHYqEQlcSzz44wAzAe+zY11FBSP2xWpG7la6nxBQm5RtlMiQ0X1Kl8DmrODAqjhINZ5G7oJe72CMmhW4CtDW9YDXzM7GfgVEAX+7Jz7cZfv84C/AQcClcC5zrlV4XffBD4OJIEvOOce3IVJl8FQtwHeuA3+81045huQX+qb1bzyV8DglJ/ChH3h7Qfh2G9BLNfPFyRh1VOwZTH8+xsw7zw46087Xl9LHax5DmrXwMGf2P575zLXvPZG/SZ47Gp45W/+fSQHPvkojN8HgrgfpTkl0dz+Z5Qk3fY6iOXhIjksX7uRmYEjEuln2gbDQPbNUBVvhmUPwqzjOwoZwDfvejzMivJH+Yt/6lmF5TOhrQlOvArmnDo0bngbtsLCW2DTG3DwJ2HqwbB1mX9mq0vCzefBmmc6z/PY1f71wtth5rEQ3UmXgzXPwzsPw+Gf9/vYBf78tQi0Nd7NxCoAACAASURBVPibpXerqpV+n6QKWAaielWXD3q4QQ2bOl/w+8e48/uzBr7ud5sg6ZuVj9kdfjYbKvaETz/VcQ41V/vH0L39EBz/PXj4ezDzOCga4wcVm7ifz0dyiyGa07d1O+e7vpRM7LgeDjXxZsgpyHYqdo1kwr8+8yt/LTnparj2BP/Ze78N+53vrxEFozPPHyRh6QO+EHL83N6vt7Xe562zju98Xe7tdbqtyee/FvW/y0wSbZ0GwQswH/i6zstviSe569X1nFtQxubNGxmdSJIXi/Z+W3alFQv89XLSfBgzK3v3NLXroKAccgv9+/rN8OcTYP4FvlC6qRKmHOR/H7FcPwp/kITisf4YB4m+5x0CgLksPibFzKLAMuBEYB3wInC+c25x2jSXA/Occ582s/OAM51z55rZ3sDfgUOAScDDwB7OuWR36zvooIPcSy+9tPM2aJAlkgHfu3sRh8wo54z5k9s/b0sErNjWwPQxRViyla01jUweX4FzUNscp6wwx58XzhGLRnywF8sDixCvXEVs1ARqEnkEyQRLVm/gyH39jU99S5xlm+spzI2x+9hicmMdwVg8GdDQksABBbTy5pP/Ys5x51GcF8M5h9VvxLU1siwxgVEFOUwYFZYSNlYSX/MC0elHEHn1r9DaQHz2+4guupMH10U4Ze0vB21/tU44kOSFt1NQXIY5B8seID5+P5ryxlOYF2X9i3cz/d8f3W4+VzQeO/ZrcN9X/AcHfNTX6j3wdbj037iJ86B+E9ZcDfdfAef/AxfEYcOrsPgumH8R1c/8lfK3b+sxfW7mcbjVz+L2OJnokrv8h9c3EmwKiEyIwMT5NFkBl3/y50wfU8Txc8axbHMDFyw4mttaDqXmuB9xwaHTKM3P6XRs1lY1UZQXo7wolzWVTVz39Eq++r49iUaMVZWNFOREmTq6kG0NrQQOyotyiUaMVAxtaRm/c47A+d7GkYjRtm0VOcvugWd/54/xHu9jVTCe2OzjmDR1d6L5JbjWehIPfJuVM85l85T3sc+kURTlxahdu5hg7fOMPvxSAufYXNfC1NGFLF/5DqNLSiixJhryJlBeUtCeDudce3pa4kkq65sZvfI+Gl+4AVexB2MPOQduOd8fC6B+7sWUnPI94vnlBNVraCuZQk7U75v8nGjH8lY/4y8kc07DOQern8aW/dsXsrz9EADxk3+G27KY3Feu69PvbutBX6F0n5Nxkw8kngwoyc8hngza00FbE0SibKmqYW2jMX+3sUQjRiIZsK2hjdZEkt3GFFHbHKcwN4pzsKGmmedWVHLuwVOpamzzx8PFKSspxjmHc9DYlqBk0wtw/fu3S1Pz4V+l4Jn/yZjeoGgckcYuTecr9oCzr2NTwWzKCv3FtDWe5L7XVrP7hHIOnelvjDbUNFNelEt+jr+pcfEWiObSnAjIi0WJRgwXb8beeZjEkvuIrHmWSM2qnnfgZ18kXjSeHAs63RzW11axbfM6KN+d3coLWV/TzNiSPPJiEcyM9TXNrN7WyCEzyn0+h8+nNr18L7XPXM+sy24gGckjGrH230JVYxvvbGkgFjWmjC6kIDdKab7f3mTgWLKxjn0mj2Lhuhqa2pIcuNtoYgRse/E2yg44i5zcPKoa2ygvyqU1kcQ52FLXSk7MGJfvqH7oxxRseY3C+R+C/FHYm7fj6jdRXzCZyjkXM2XmXuSUTYanf01i8b3E1j/vN/a0XxOfdRKxkvEkWhtpe+dJkrNOoHXhXVQccBq24VV/g1o+Exq34l7+C7bkHrAIbtJ87Nhv0rZ+Ibn3XO6Xd30jiU2QnJRP3rSDqGuJ89gfb6U4L8afnljBn3//cUprl3Hm+T/jjh9+0f8uwutHSX6M1kRALGLkxSLUtyYoyYthZrQlgk55T1fOOZ5ZXslhM8fgnGNjbQt5sQhFeTGSzlHTGGfamEISyYBYNIJzjrZkQG40Qjzp84gJo/LJiUZoSwTUt8QZVZBDNGLE4wkabriAgqMup2CP4zqt99U11UwZXcim2hb2nTKK2uY4ebEIOdEIrYkkbYmAwtwYubEILfEkv3rkbT5z7O60tCUZW5KHmbF6/UbGrL6P4pmHwfN/8IWyyx/1Aeahl8HDV8KUg6kfewAlr/aikLUXWmd/gNUHf5s99tgbFt4Gr91IW8kUcl+/EXf29bTteRotCceil59k/7pHyX/jxva8L5h0IJGyKSQ3LWLDzA8z5vjPU5ibg3OOuIuCGbmWhLoNbF3+CqPnn05b4CjMDa/ZLTVUN7YxumJ8+7mTOg/iyYDi8JinJFY+TfSJn2ArH6d5zD7kl4wmmHkckSO/RGPlOvJf+T9ib90DGFSv7NjI9/+MZO0GIgbJMXuSHDObvLKJuJxCVjfmUFGSR1FulERLPZFoDkE0j5xopP2eI5WE/Jxoe74TTwZEzVhT1URJfowxxb5QeW1VExNH5ROL+uOcmr4lnuTVNTW8Z/cx3LdwI/tOHsW0MYW0JQKa40kSyYCIWXveV9fsg9nWRJIxxT4PSa03J2Ik6jYReeInRF7+S6+PdfL822DxXUSmHcr66mYmz57nr0FP/6p9mpY9TidaPIbY6qfZ9N6fM3Hu0X69NevJadpKonYDiZknkPvno4hsfatj4dOP8gVo9Ztgwyv+eH3iMRpcPsnRu1NeGMPdegmROacSb2kgWbue3Bf/QCTs4++Kx+Pe+10S619nRcUx7Fkexf5+Xkfar2/GNiVomFBECc20FU0mb9FqFizdwjtbGvjBfUsAeKDspzQ0NXHH/Ov40Vn74pyjNRG0H4dM0mMQM6M1kWTF1kZmjSumqrGNZZvrOXJWRaffYl1LnPxYlG0NrcSiRmFujGQyoGHdIt5sm8BJcyewtqqZqeUFNNRWsryylbnxNwhevZm8pf9qX05QNI6q2Wfjqtcw5tTvExk9FWJ5tD75a1YEE9nzsPcTyStiS30LiaRjUllBe3rT05O63jvnSASu/W8zIxk41lY1Mb40n7ZEQGl+lIYXb6Lkgc/5maccDOtezLxvJs7HNr7W6bPWM/9C7aL/MG7ZzQTfriQSi9GWCIgY7Xnqmqomdisv9Pc8+aNoW/UcsfxiNhbuSU3VVsaNm0B+y2YiKx6n6OALfEFo/UbA/H3ttmVQMsEXXj18pW8ptsdJcMhlfvTuxq3+u/zSbo/rUGBmLzvnDtru8ywHvu8BrnTOvS98/00A59yP0qZ5MJzmWTOLAZuAscA30qdNn6679Q3lwLemqY2TfvEEW+pbmVlRxOqqJpJBx7EpL8qlqnH756Pdk/st9o2s4sHkQdyZPJIaijk6spBLo/9mqZvCorz5XBi/Y7v5ks6Iml/+A8mDedtNZqpt5czo02xzpTwd7MMvEx/ig9GnmGLbOCHyMqOs54FQalwR9ycPZbHbja1Fe/Cntm/2a1/8vvTLvFQ/igPjL/PZ2N08mpzPe6Ov7XjG0HPBHCpyWpmVXAHA5W1f4PLY3ewTWdWv9PTFqmA8/wqOIOEilFkjH4890O20V8Y/wjdvuobcTW3YhAitY+fx6uY4513QqdEDT+Z+kRfcnnwlfvmgp7csP8IXE39hXmQFObEoVfE8qinm+WAOe9kaLok91Kfltboc8izObxNn8LlYxwWm0pVwXeIUEkT5Zs7fO83zTjCJPyVP5bHk/lRSwryCSq4NvkOF1fV5e9a5Cp4P5vDV+GXMtnVcFruX2thYLnV3AvBwcn9OiL7a6+V9rO0Krsv9GQAfb/sKhbRycGQp8yPv4DD2i6zYbp5PtX2Jj0Yf4ojooozLPL71f6h1xVRRQpChpn8yW/lyzm1Moop6CjgmshBwxEhyRt41XNL8N2IkKKSVk6Ivt8/3RHJfjo6+0e22fKTt6ywLprCJMRgB1+T8vFf74uHk/vwscS6zbR2vutlUuhLOij7F1TkdBQTrXAXvBJN5MtiH7+TctMNlducvifdRVJDPI40z+FOuLxT7aNvXeTbYm9m2jrFWS40r5uD8NVS1xrgzOJKACMcUrOCv7tu0uBzyLd6+vIeSB3Jd8hSeC+aQGjzug5Gn+ETsfqpdMZsp59vxSwmI8IXYHdyVPIIzos9gOB5N7s950cfIsQRnRp8G4LK2L3Fa9FnKqWOVG8+J0ZcZ24/f6c707fil/OCm35Lc5EhMKqS+fB/e2dLQKV+57+YvMDeymvMv+CHPBn2oZQJiESMROPbIq+a40VtYXp/LtGAtRyWe5bjo6xnn+VXiTG5PHs3hkUU8FexLHm3sbav5Te5v+VjbFTwaHMBYqmkhjwlWxaXRB3g4OJA3ghmMtgauiN3KwZGljLYGAO5IHsmtyWO5KPof/hL5EJ8MbuMXiQ+x0ZUzL7KS/e1tpkc2cVX8I/wm5zfMiazm4rZvMd020UoOX43dyqfj/8Uno/exyE3n09F7mB7Z3K/9vZlyxjM8non8r+Th/C1xIlNsK7/K/T0AV8cv4JrkB+g6uOLpkac5M/oUbxUexAnN/2Z2ZP1OS9fzwV4cGvGB3NJgCue0fY9cEmxjFABTbTNnR5/g2WAuB1YEJCpXst5V8FSwDzWUdFpWhICJVJJvbSx3kzt9N80280Tel7gqfjHXJU8hhwQHR97iqtj1fDvxsU75RFdlhTmc0Xov38/56w6359rEKfw58X6ezf98P/bG4LshcQIXxx7u9/yb/pJLxeZqmicUUEIzLUWTOetTN7J4Y+e875qcnzPZtvH+th91s6Se5edEaIl3jDSfS5xxVsMetpapsTrmu0VcmzgFAw6JLOHoyBs4jAYKODX6XPt8f0ycxlxbyUGRZRRY5ucKL2YGe7Oy02etLgcHna4hv3bnslfwDn9MnMbK/L2pa24lSRRwnBh5mfWugsMiS7gtOIbD7U1OjL5CdWQ0zUmj3hXyRDCPYyOv882cv3NL4ljeH32B0h3cS/fW2a3f5SW3FwfYMq6I3cph0SVEcATOiNhOjO2ieb6V0mef79xSbogZqoHv2cDJzrlPhO8vBg51zn0ubZo3w2nWhe+XA4cCVwLPOeduDD+/FnjAOffPLuv4FPApgGnTph24evXqnb5d/fHa2ho++LuniUWMSWUFJJIBG2pb2r+fOCqfjWnvAYyAlfkX7eqk9tuVpVdxQu0/OdIWdvr8T4kPUOlKOS7yOp+Mf5lRZWNYX9PRFLggJ0pLPM5Hov9hv8hyvhn/BPvaCl5xe7QHDu+NvNIeoOzIkmAaLeTy08S5RAj4f7GbuSl5PCdFXuK+4FA+G/0Xu0W28FYwlRVuIu+PvgDAM8Fc5trK9gKAp5NzKbIW5keWA/6G88bkCaRfOOflb2ZK20qqKOXmnKvbM6MHkwdxWfzLvHXL2eRtasUmRFgYzKCJ/O0C3wdyv8FaN5ZPxb/S6fPuCkNS8mIRWhP+IhKNWHtBynTbSAnNTLAqjoy8wUdj/9nhPvtS22dYWnQQ98c/3unzJpdHobXucP7BFHdRfpC4qFc3ID35W+JESosL+UHjB/lw8ACfjt1LqTXxZHIfLol/Pby4+YtvgJHI0DPk9zm/bP999NVbwVTecZMopJU33EzeDKZzZvSpPi9vSTCNM9u+Twt5HGRv8c+8q3g+2Is/JE7n+eiBEG+knHrWM3a7ecdSQ561MSWnnlsi3+nXdvTkruThfD/+EXJJkGdxql0JDeQTwVFAKx+J/oev5fxj0Nfb1ZJgGnMia/hP8kBOTCssAF8IuI6x7GaDP4DcLYljmRHZ1H5T39UlbV9jhZvIE3lf6nE5r+bMZ//4a2ymnJVMYVRQw9fjn2SiVfLeyGucG1sA+PO0hVz+cPMPCTYFtE0s4rXkdIBO+co9N/8X+0ZW8vELvssjwYEAFOfFaGhN9Gq7psRquD/n65S6wRl7YEFyP47tJmje1V51s3kocSBvTTqDfTfczu3Jozht/Faat61lv8hyzoo+xU2J4/ld4gySRKiklD/n/Jz3RBZzatvVlFPPidGX+EHiIn4cu4bzYgv4YOtVvOWmclBkGc8Ge3N0ZCGfid3NIZGlndZ9feKk9sLGmxLHc2HskU7fvxFM5xeJs2kjh49FH+CgyLJBu4mucsV8LX4Z7428yrHR15hk2wfzNyaO56IwTS8Ge3BwZFn7d48k9+cH0cs5pmQD6yrrOCyymCMibzInspYml4cD6ihiYobl9iTTOZvuzuQRPJXcl/Nij7KHretUQP+t+MdpdTk0k8uJ0ZfbC7B25J1gEj9OnM9yN4nR1HNm9CnOyHmBUtc50LsreThzbTULgv14NtibBcF8AozUPcAhtoStlHF29HE+G7ub25NHMTuvmnmJNwGfL/0m8UHqKWSDG8MjeV/lqeRcjuym0DTdh1q/x5tuBhdGH6GEJtaUzKM0qOOJ+kn8M/dKxtj25+ZmV8Z4q2l/f2/yUObaKja6MRweXZw23Wj+K345C4OZ7BdZzhduvJEDtyyhdUI+xbRQnTuB/TN0LftVzm+ZZ8s5ru0XvdrPAFNsCwkXJWYBXyh4gA8H/2ZJMJWz2r7Ps3mfp8wae72s3ngmuTcfi3+V6RMq+HfNaYO67L54Irkvf0qeylWx65ls27gs/mXWuQouj/2Lh5IHc3RkIdNsM4XWylZXxt+SJzLHVg+oYLlfphwCM47yLVJqw6eLFE+A/S+C4wf/nmEwDdXA9xzgfV0C30Occ59Pm2ZROE164HsIcBXwbJfA937n3O3drW8o1/gCnZrmgG9ytKqykd3HFre/j5hvkhYxMBeQWHQ3rQt+TmFxKbY6zNSLxhEfP4/G6CjK5hyHm3kMQX45kdo1NFoRxVtfwT33R9i0kMS0I4kWjcYKx8BrN2Gp/hy5xb7/x9g5cPDHIdGCq91Aa+k0cnPzqXz8D3DxnVTULsJKJ+KKxpH8v/cSq1/n5584H475Guz5frasXsS44nyo8E2qg2SAbXuLxD1fgvNv4e63GpkzsZRxpXmMLszt1Kwongw6mmZ1aQqbUtXYRnVTm99Plct9/4dojKB4EvzuYCI1q3CjptAw5zw2zv8CLQnHo29t4RNHzaQoN8rLq6s5cLfRBM7v47qWOOWFuayvaaasMIeSoJ5kXhnRaIRNtS2ML8nFEi2QW0hLPEmUJDkt1VAyvtOxi6b1x22J+2aRL62q5MjZY3l+ZRVb6ls56YxZ5G1q6RT41v/7YU7cezxLN9UztiSP5T85gpLCQhrOu5PpFUWMKcrttA/qW+IU58UIHKx+4W7WVTZxZMEKIhPnwbO/g9knwujp8M+Pdfvbcyf+N7b/Rbi7P4+9dS8umovNvxA378M0jD+YkrA5KEHAqrdeIie/mNFT9qAw1weDK9asYeZ1+/pl5Rb7vimz38erFaex/6prff/SdS9h04/AbXoTVzwON3ZvbMbRROrW+ubGm8LaSouQPOLLBDlFRBffQeve57DZjSZ/39MpLCwkCAJKCvKobGhlfXUj++dt9M1zph8Jt1wIW9JuGmb75jnJZIIt1TVMfPMagpOuhtEziJT645VqOhxxSYhE2VTXyriSPG54bjVnHTCZjbUtTByVTyLpWLGtkflTy0gEvmlvMpEgsv5FmktnUFi7nGDZg0RWPApzzyI5/2IirTXYmFnQVEnyiZ8RefHPWNBRmtwbyYq9iNRv8E2I9zwFizf7Pn6JZvjiQt8UNTw+QeCoamqjvND3/YuEBR6pptWpApCWsGlUZUMbpQU5lObHsJZa3L+/CQd8BF69AXttBxfYD11LULsBizdimxZC+Uzc6qeJTzqE3A/8BMwIwn7pLfEk0YgRNevUTz0IHLbkX1jTNoLcEiJmvi/Twn/gyqZhi+/KvO7DPgvP/a7jfdE42nY7mmi8kcqpJ1L50h1Mm3MQRc/3oyvFnNNhyd0AuMIKDEfTEV8jb9xsojed5afZ61R4617cpAMJGrbQPOUIis/8Fes2buSVlVs5aN4+lOYGFBQUETFoW/c6tRvfoWD2UZQUl7KtNYJzMLYkz+dpQLK5hqgBsXziD/+AWN0amk/6KbkkiI2eSlCzjsioyRn7pLmmalzNWu7fVsGDt/2Z39xwFcGmgKYJhbwZzGB8aT53/eImPnPs7izaUMfok45kZutSXrvyC+xT9yTRWcdh886lcvnLlL1zJ9awkcix36CuajOuZi0lQS2Rmcf4Poxv3ds+0ivv+Zy/TtRvwm18HVc4hsjmN0mccwPJtx8mZ81T2MT9sEXbtzoCCGYcQ2Tl470+NG3j5lF51j+YcO1BWLwRN+VQrHYt1G/o2BczjsZWPtHrZQIw+UDctMNJHvcdYrl5GSdJNc9O/abrW+IU5fom3Ms217OuupmT9h6Ppf3ua5vi1LXEGVeaR9SMeNLfb9W3xmlqTVJfuZFJS64lOnk/SvY/m2gsRjJwvLqmmrmTRlGw9nHciiexp/+X1k+/QN6EPUkGvsl+6nxqaotTlqykYMxUaG3wI3U3VcLmRbDv2bgg4J1FLzJj3b+IPR+eM4UVJC66i9q4MeaZH8LS+zLv7/0uJnfeWf5Gt7Ueph1KTVMb2xramDWu2F9rW+vYumE1FdPngvluCC1x3+NsTVUTe4z3NULNbUkKctOavL5yA4nHfkyyYAw5h19OYvQM2ta8QtGr12BVyzMfp7Fz/FgBz/0BNnffumVncuffgq14nC2zzsaN34fS/By2NbRSnBfjvjc2cuGh02ho9d3CinJjRCMdvwfA95ttrSNZMKb9HuHN9bUU5kaZOaYQIhG21DYRadhI6dhp5MZrfX/OkgnUV2+maM3jMOd0gqi/V+p6PwSwYmsDU8sLidauhX9eCmXTaD78CpIVe1KSF6OpLUlVfTNFBf63Pqogh0jct6YgSGJp4y5UN7axdM6BHLhlCW0T8iiilbVuLEed/2fOPWgq/+/UOby8qpp7Xt/A5Q2/ZvKWx6m9fFF7d7fUfSvLHiSINxNZ+wJur1OJbF0Mj/53pz7EPZp6GIzfG1Y/C1uXdHw+fl/Y/AbJij0JymcRXfcClluA1ayhZdYHqI+WEUw6gPFHXgIWoSU8B/NzohBvAYuwtjbOmJI8CtuqYdkDrN5aQ6vLZY/dd4dHvt9xb9Jbp/0altwDoybDy9d3/m63IwjOvIbqWAXxpGN0UQ61TXFa4gFlRTm0xJM0tiaJGEwZXciKrQ2UFuRQUZyHJePE175IZVU17pGrmNzcUXDmLIJ9ZSk89kPf9/eIL0LJRNyiO2lceA91hVOZ9PpvqBpzIGW7H4wd/HHqimf4a39rPdSth6oVsPvxfvyctnqYdULnGt0tb8Hrf4fjv9vxZIAhbKgGvmrqPJiC8GYkkqXHMzsHbY16PEYfNE8vJH9TMzYhysJgJuMqypnwynOdpnE3no1teMUPsrT/xbD0fj8q714f8Be1gZh1Ipzzl8FprhIkB5YZNmyFlY/D3mcMbNCG5mpY9TRMO2xwBg0abK31kFMI/7jIH0vwfTerVvi+NmdfB7sd4f/uLv3xsPXHrhw1tLUenvolvOezUFi+a9YZb/b7oanS91+btH9H4HffFbD+JfjgH2DcnMzzOwfP/NrffJROhlVP+n3/sQd9P37nfJ+46tWwx8l+fYXl8NC3YY/3wYyjd812DqIlC/7BnEsu7BT4HjpjDPb4gvZp2t5zKLkbX4ZLBjAw2xFf9AO89UVLnR8kLqfQ3xSC/93fdA4cdrn/jc2/0A/gkkz4Absi0e1HHw8C34+0fKb/PdSu97+Ryndgn7Bwom6jnz9/lO+v1tYAZdOgZq3/Hcy/wC9n61t+OSNhBN7eSh8AKdHmB2ZaeKvv2wdQWAGXPdFxjLJhy1vw+0P934d+Bo6+onN+uOrpjvEN8kfBmNk+Pzj1F/46ueBH8NQvYOqhsCa8LfzGWr/ddRth8b/8KP27HQ5j9/TjPZRO8tfCX3Rp/r/3GXDOX32Q9i4cjC9x9DHw4jPEx+dQQBtr3Vhuu2YBXz5pz84T/vubfnDPb6yFuz/nC8kO+IgfUKo3weP0o/y+vv+Kjs9OvMrnNemSCT+QXOppBTtbEPjfTVOlz6/Kd/f9W4OE7+saBL6PbKbzJQh8wf9uRwz+QFqJVp9nPvZDf10es3v30zrnCyyHQcA6WIZq4BvDD251PLAeP7jVBc65RWnTfBbYN21wq7Occx82s7nAzXQMbvUIMHskDW4lI1/XwHePaRPIf6pLbcXN58Gy7vsKd2vUNGipgdaweVbxBDjqy/6mwAV+pOKdNZKviGTF8mfuYPcLPozbFNA4oYi1ebOZM7EUFizomOjoI/3o9qnA97RfwUPf8XmFRWHuB30hw0Pf9t+Pmuabuc06EQ68xLfoKJmwqzdNdrZUQDFUxMMuTwMdIXrlk/63vdcHej9P9SpfoDL1EI2ee+yxJJ5/msT4HPLDwPeVf73UadBVAB75b3iyhy5nkw7w9x71G31B5AEXw4xjfIFD+jFuawoDySmdn4Yh0gfdBb5ZzeGccwkz+xzwIP5xRtc55xaZ2VXAS865u4FrgRvM7B2gCjgvnHeRmd0KLAYSwGd7CnpFhjqHHzhmO+Eoje1mHONrozYvguJx/uZg6qG+FmXRHZCM+1rcd2EJn8i7XX5Ox026AwrzMlzmLa2W5MqwqeG+5/hnVZdO9jWu4GtfEq1QMXvnJViGjqEU9MLgPRJpxlF9n2f0dP9P2qU/w3dsSYaANL2AoLAC5n0Ynvu9f1TlEV/seHTPjuQW9lx7KTIAWc/lnHP3A/d3+ey7aX+3AOd0M+/VwNU7NYEiO5HDOo0hGc0U+L7/Z77P4VnX+KZZPd0M7Hlyx99mvvZGRN418vJSz3b1N6kZC9Ms4p9h+YW0liS5Rf7ZlunKpu2cRIrIMGPtga8DKoozBL4HfBQ2LvR9QMft5T87uX8jPIvsLFkPfEXEi5hlfpDC3qf7fzB4JeAiMiLldanxTT3jeDslE7bvOysi0o30Gt+pozPU3pZOhPNvRv0dDwAAIABJREFU3oUpEum7LI2CJCJdRaODPPCBiLzrWNS38rDw/4w1viIifdQR+FrnEbpFhhEFviJZlD60XKbHEoiI9EWkS5/+3O5qfEVE+kB3KDIS6IooMkSknkYlItJvXQLfvJgu8yIycOl9fEWGK10RRbKqowy1KNPoqyIifWCRjnwkNxohR4GviAyYEVHIKyOA7rRFhohp5b0c6l9EpDtpNb75OeqHJyKDIK2d84RR+dlLh8gAqShYZIiIaBAaERmgSETl2SKy8+TFVKAmw5cCX5GsUrArIoMoosu6iIhIJrpCioiIjBDWaXArFayJiIikKPAVGTJ0kyoiA5M+uJWyFBEZfMpYZPhS4CuSRRojUUQGk0XU/05ERCQTBb4iWaWSUxEZPAp8RUREMlPgKzJUmIJgERkY9fEVkZ1L+YoMXwp8RURERgiL6nFGIrITKe6VYUyBr0g26QIiIoPI9BxfERGRjBT4imSVdfO3iEg/mC7rIiIimegKKZJFGtVZRAaVAl8REZGMdIUUEREZkdSKREQGm/IVGb4U+IoMFRrVWUQGk7IUERGRdgp8RbJKd6YiMohUgCYiIpKRAl8REREREREZ0RT4imSRBrcSkZ1Htb8iMsjUqkSGMQW+IkOGLiYiMlDKR0RERDJR4CuSVbpJFRERERHZ2RT4ioiIiIhIL6jAXoYvBb4iQ4WuJSIyUOp/JyKDTvmKjAwKfEWGDF1YRGQQKQgWERFpp8BXJJt0YyoiIiIistMp8BURERkxVJgmIjuRCuxlGFPgK5JFnZ/jq4uJiIiIiMjOoMBXJKsU7IrIIOpUG6P85f+3d//Rl9V1vcefLxgGKEF+iDaKBBimVN4BR5OFWSB0lUr8gb+yxG5eVqU3tSwhqmt32fLHyixvaiJKWGiWSs5SkV+i3m6JDsiPgQkHEW7E6AyS+YsI5H3/OJ8zHb6e74/5znd/z/nu7/Ox1l5n78/+7L3f3zmf2Xu/9/7sfSRJGjLxlaaF56iSJElSJ0x8palh5itJkqaZ5ypauUx8pYnyACJpKblPkSRpHBNfSZL6yLevSpK000QS3yQHJbk0ydb2eeCYOuuT/GOSG5Jcl+T5I/P+IsmXk1zThvXL+xdIS6vmryJJkjRZXk/TCjapO75nApdX1VHA5W16pu8AL66qHwGeBvxJkgNG5v9WVa1vwzXdhyxJ0pTzLq+kTrmP0co1qcT3VOD8Nn4+8MyZFarqi1W1tY3fAWwHDlm2CKVlUDsPIB5IJEmSpK5MKvF9WFVtA2ifD52rcpInAmuBL40U/2HrAv2WJHvPsewZSTYl2bRjx46liF1aOua9kjrjjkWSpKHOEt8klyXZPGY4dRfXsw74S+CXqur+VnwW8BjgCcBBwGtmW76qzqmqDVW14ZBDvGEsSeozk11JksZZ09WKq+qk2eYl+WqSdVW1rSW222eptz/wMeB3q+qzI+ve1kbvSXIe8OolDF2SJEnS9/DimlauSXV13gic3sZPBz4ys0KStcCFwHur6m9nzFvXPsPg+eDNnUYrdca+zpKWkC+3kiRprDkT3yTHJXlbe5Z2R5L/l+TjSV6W5MG7sd03ACcn2Qqc3KZJsiHJua3O84CnAC8Z87NFFyS5HrgeeAjwut2IRZKk/jEHlrTU3K9oBZu1q3OSi4A7GNyN/UMG3ZH3AR4NnAB8JMkfV9XGXd1oVX0NeOqY8k3AS9v4XwF/NcvyJ+7qNqVp5M0ZSd1xByNpqblf0co11zO+v1hVd84o+xZwdRvenOQhnUUmrQL77b3XYMQMWNKScF8iSdI4s3Z1Hia9Sd44c96wbExiLGkX7NHOUT1VlSRJkrqzkJdbnTym7OlLHYgkSZIkSV2Y6xnfXwV+DTgyyXUjs/YD/m/XgUmrQr5nRJIW7wGPTbhfkSRpaK5nfN8HXAS8HjhzpPybVXVXp1FJkiRJmrh4QU09MVfiuyfwDeBlM2ckOcjkV5IkSeq3PRfyYKS0AsyV+F4FVBufeXmngCM7iUhaVbxyKmkpZeyoJC0J9ytawWZNfKvqiOUMRJIkLSXPUCUtNfcrWrnmuuO7U5JnAE9pk5+qqo92F5IkSZIkSUtn3l77Sd4AvAK4sQ2vSPL6rgOTVpV4BVXSEnBfImnJuV9RPyzkju8pwPqquh8gyfnAF4CzugxMkiRJkqSlsND3tB0wMv7gLgKRJEm7y58dkdQl9ytauRZyx/f1wBeSXMGgtT8F7/ZKkiRJklaIeRPfqnp/kk8BT2CQ+L6mqr7SdWDS6uIVVEmSNOU8XdEKNm/im2Qj8H5gY1V9u/uQJEnSosTf8ZUkaZyFPOP7ZuAngBuT/G2S05Ls03FckiRJkqaKV9S0ci2kq/OngU8n2RM4EfjvwHuA/TuOTVoFPIBI6or7F0mShhbyciuS7Av8HPB84Fjg/C6DkiRJi+Dv+EqSNNZCnvH9APDjwCeAtwGfGv6mr6Ql4smqJEmS1JmF3PE9D/j5qvpu18FIkiRJmlZeqNfKNevLrZI8GaCqPjEu6U2yf5If7TI4SZK0WJ6gSlpi7la0gs11x/c5Sd7EoIvzVcAOYB/gh4ATgB8EfrPzCCVJkiRJ2g2zJr5V9aokBwKnAc8F1gF3A1uAd1bV3y9PiJIkaZd5Z0aSpJ3mfMa3qv4VeFcbJHXGM1RJkjTtPF/RyjXrM76SloHHD0mSJKlzJr6SJPWSV9YkSRoy8ZUkSZIk9dq8iW+S70vye0ne1aaPSvKz3YcmrQZ5wIckSdLUiicsWrkWcsf3POAe4Lg2fTvwus4iklYlDySSJElSVxaS+D6qqt4E3AtQVXfjWbokSVPOQ7UkSUMLSXz/I8m+QAEkeRSDO8CSJEmSVg0vqGnlmvN3fJv/CXwCeGSSC4DjgZd0GZQkSdpNnp9KkrTTvIlvVV2a5GrgSQwOo6+oqjs7j0xaVTxDlSRJkroyb+Kb5Ng2uq19HpbkwcBtVXVfZ5FJkiRJkrQEFtLV+e3AscB1DG5L/WgbPzjJr1TVJR3GJ0mSFsWeJJIkDS3k5Va3AsdU1YaqejxwDLAZOAl402I3nOSgJJcm2do+D5yl3neTXNOGjSPlRyS5si3/gSRrFxuLJEmSpHn4O75awRaS+D6mqm4YTlTVjQwS4Vt2c9tnApdX1VHA5W16nLuran0bnjFS/kbgLW35fwV+eTfjkSbH44gkSZLUmYUkvjcleUeSn2zD24EvJtmb9tu+i3QqcH4bPx945kIXTBLgROCDi1lemj5mvpIkSVJXFpL4vgS4GXgl8CrgllZ2L3DCbmz7YVW1DaB9PnSWevsk2ZTks0mGye3BwNdHXq51O/CIcQsnOaMtv2nHjh27Ea7UBRNeSV1x/yJpqblf0cq1kJ8zuht4cxtm+tZcyya5DPiBMbPOXlB0A4dV1R1JjgQ+meR64BvjQh23cFWdA5wDsGHDhrF1JEnqHc9PJUnaaSE/Z3QU8HrgaGCfYXlVHTnfslV10hzr/WqSdVW1Lck6YPss67ijfd6S5FMMXq71IeCAJGvaXd9DgTvmi0eSJEmStPospKvzecA7gPsYdG1+L/CXS7DtjcDpbfx04CMzKyQ5sD1LTJKHAMcDN1ZVAVcAp821vCRJkiRJC0l8962qy4FU1W1V9VoGL5baXW8ATk6yFTi5TZNkQ5JzW53HApuSXMsg0X1De6s0wGuA30hyM4Nnft+9BDFJktQT9nWWtMT8OSOtYPN2dQb+PckewNYkLwf+hdlfRLVgVfU14KljyjcBL23j/wD82CzL3wI8cXfjkKaDBxJJkiSpKwu54/tK4PuAXwceD/wC8OIug5IkSZIkaaksJPE9vKq+VVW3V9UvVdVzgMO6DkxaVbzhK0mSJHVmIYnvWQssk7SrTHgldcYdjKSl5n5FK9esz/gmeTpwCvCIJG8dmbU/gzc8S1oyHkgkLTF3K5Ik7TTXy63uAK4CntE+h74JvKrLoCRJkiRJWiqzJr5VdS1wbZK/qirv8EqSJEmrmT9npBVsrq7O1wPVxr9nflU9rruwJEmSJElaGnN1df7ZZYtCWrW8ciqpK+5fJEkamqur823D8SQPA57QJj9XVdu7DkxaXTxBlSRJ08hzFPXDvD9nlOR5wOeA5wLPA65MclrXgUmSJEmStBTm6uo8dDbwhOFd3iSHAJcBH+wyMGlV8WKqJEmaep6waOWa944vsMeMrs1fW+BykiRpUnz7qiRJOy3kju8nklwMvL9NPx/4eHchSauRJ6iSJGnKebqiFWzexLeqfivJs4EnM2ju51TVhZ1HJkmSJGmKmPlq5Zrrd3z/DHhfVf1DVX0Y+PDyhSVJkiRJ0tKY61ndrcCbk9ya5I1J1i9XUJIkaXd5Z0aSpKFZE9+q+tOqOg74SeAu4LwkW5L8fpJHL1uEkiRJkiTthnnfzlxVt1XVG6vqGODngWcBWzqPTJIkSdIUsSeJVq55E98keyX5uSQXABcBXwSe03lk0qqQGZ+SJEmSltpcL7c6GXgh8DPA54C/Bs6oqm8vU2zS6mHeK2mp+Tu+kiTtNNfPGf0O8D7g1VV11zLFI0mSJGkaeT1NK9isiW9VnbCcgUiSJEmaZma+WrnmfcZX0nLwQCJJkiR1xcRXmiTzXUmSJKlzJr6SJPWSV9YkSRoy8ZUkSZIk9ZqJryRJkiSp10x8palgl0RJS8zdiiRJO5n4ShOVB3xI0tJxxyJJ0pCJryRJkiSp10x8JUmSJEm9ZuIrTQW7JEqSJEldMfGVJKmXvKAmSdKQia8kSZIkqddMfCVJkiRJvTaRxDfJQUkuTbK1fR44ps4JSa4ZGf49yTPbvL9I8uWReeuX/6+QJGmK2dNZkqSdJnXH90zg8qo6Cri8TT9AVV1RVeuraj1wIvAd4JKRKr81nF9V1yxL1JIkSZKkFWdSie+pwPlt/HzgmfPUPw24qKq+02lUkiT1hrd8JUkamlTi+7Cq2gbQPh86T/0XAO+fUfaHSa5L8pYke8+2YJIzkmxKsmnHjh27F7UkSZK0mngNTT3RWeKb5LIkm8cMp+7ietYBPwZcPFJ8FvAY4AnAQcBrZlu+qs6pqg1VteGQQw5ZxF8idcmjiSRJktS1NV2tuKpOmm1ekq8mWVdV21piu32OVT0PuLCq7h1Z97Y2ek+S84BXL0nQ0qTEBFiSJEnqyqS6Om8ETm/jpwMfmaPuC5nRzbklyyQJg+eDN3cQoyRJkiSpByaV+L4BODnJVuDkNk2SDUnOHVZKcjjwSODTM5a/IMn1wPXAQ4DXLUPMkiStIPYkkSRpqLOuznOpqq8BTx1Tvgl46cj0rcAjxtQ7scv4JEmSJEn9Mak7vpLAGzKSuuP+RZKknUx8JUmSJEm9ZuIrSVIvectXkqQhE19JkiRJUq+Z+EpTwTszkiRJUldMfCVJkiRJvWbiK0lSL9mTRJKkIRNfSZIkSbPwIpr6wcRXmigPJpI64u5FkqSdTHwlSZIkSb1m4itJUi95y1eSpCETX0mSJElSr5n4StMg3pmRJEmSumLiK0mSJEnqNRNfSZIkSVKvmfhKktRLPkIhSdKQia8kSX1k3itJ0k4mvpIkSZKkXjPxlSRJkiT1momvNFH2RZTUFfcvkiQNmfhKU8ETVEmSJKkrJr7SJJnvSpIkSZ0z8ZUkSZIk9ZqJryRJvWSXEkmShkx8JUnqI/NeSZJ2MvGVJEmSJPWaia8kSZIkqddMfCVJ6iX7OkuSNGTiK00Dz08lSZKkzpj4ShNlxitJkiR1zcRXmgomwJIkSVJXTHwlSZIkSb1m4itJUh/FniSSJA2Z+EqSJEmSes3EV5IkSZLUaxNLfJM8N8kNSe5PsmGOek9LclOSm5OcOVJ+RJIrk2xN8oEka5cnckmSJEnSSjLJO76bgWcDn5mtQpI9gbcBTweOBl6Y5Og2+43AW6rqKOBfgV/uNlxJklYSn/GVJGloYolvVW2pqpvmqfZE4OaquqWq/gP4a+DUJAFOBD7Y6p0PPLO7aCVJkiRJK9W0P+P7COCfR6Zvb2UHA1+vqvtmlEsrlHdmJEnSNPNcRSvbmi5XnuQy4AfGzDq7qj6ykFWMKas5ysfFcAZwBsBhhx22gE1KkiRJkvqk08S3qk7azVXcDjxyZPpQ4A7gTuCAJGvaXd9h+bgYzgHOAdiwYcPY5FiaHK+eSuqIv+MraSm5S9EKN+1dnT8PHNXe4LwWeAGwsaoKuAI4rdU7HVjIHWRJkiRJ0iozyZ8zelaS24HjgI8lubiVPzzJxwHa3dyXAxcDW4C/qaob2ipeA/xGkpsZPPP77uX+GyRJkiRJ06/Trs5zqaoLgQvHlN8BnDIy/XHg42Pq3cLgrc/SCmbve0mSJKlr097VWVodfG5G0pJzxyJJ0pCJryRJkqR5eDFNK5uJrzQVPJhIkiRJXTHxlSRJkjQLL86rH0x8JUmSJEm9ZuIrTZRvdZbUkXiXRpKkIRNfSZIkSfPwYr1WNhNfSZIkSVKvmfhKkiRJknrNxFeSJEnSeL4uQD1h4itNBY8qkpaa+xVJkoZMfCVJkiTNzXdbaYUz8ZUmyYOIJEmS1DkTX2mizHwldcSezpIk7WTiK00DT1AlLTl3LJKWgvsS9YOJryRJkqR52EtNK5uJryRJkiSp10x8JUmSJEm9ZuIrSVIv+VyeJElDJr7SVPAEVZIkSeqKia8kSZIkqddMfKWJ8g2JkjpiRxJJknYy8ZUkqZfMfCVJGjLxlSZp5w1fT1AlSZKkrpj4ShPVMt+Y+EqSJEldMfGVJspnfCVJ0jTz4rz6wcRXmiS7OkuSJEmdM/GVJmrY1XmyUUiSJEl9ZuIrTQUzX0mSJKkrJr7SJJXP+EqSJEldM/GVpoFvdZYkSdPIUxT1hImvNFHe8ZW0xPY9YNIRSJI0dUx8pang5VRJS+ShR8Pe+086CkmSpoqJrzRJO5/xNfGVtESyh49PSJI0g4mvNFH+nJEkSZLUtYkkvkmem+SGJPcn2TBLnUcmuSLJllb3FSPzXpvkX5Jc04ZTli96qQtmvpIkSVJX1kxou5uBZwPvnKPOfcBvVtXVSfYDrkpyaVXd2Oa/par+qOtApU75c0aSJGmqeXFe/TCRxLeqtgBkjmeQqmobsK2NfzPJFuARwI2zLiStVD6PJ0mSJHVmRTzjm+Rw4BjgypHilye5Lsl7khw4kcCk3eYdX0mSJKlrnSW+SS5LsnnMcOourudBwIeAV1bVN1rxO4BHAesZ3BV+8xzLn5FkU5JNO3bsWORfI3XEtzpLkiRJneusq3NVnbS760iyF4Ok94Kq+vDIur86UuddwEfniOMc4ByADRs2eHtN08muzpIkSVJnprarcwYPAL8b2FJVfzxj3rqRyWcxeFmWtAJ5LUaSJEnq2qR+zuhZSW4HjgM+luTiVv7wJB9v1Y4HfhE4cczPFr0pyfVJrgNOAF613H+DtLS84ytJkiR1ZVJvdb4QuHBM+R3AKW3875klG6iqX+w0QGm5DJ/xNe+VJEmSOjO1XZ2l1cXMV5IkSeqKia80FUx8JUnSFFv7/ZOOQNotE+nqLKk5+FFw29dhX3+KWpIkTamHr4c1+0w6Cmm3mPhKk7TnWthrX3/OSJIkTa+1D5p0BNJus6uzJEmSJKnXTHwlSZIkSb1m4itJkiRJ6jUTX0mSJElSr5n4SpIkSZJ6zcRXkiRJktRrJr6SJEmSpF4z8ZUkSZIk9ZqJryRJkiSp10x8JUmSJEm9ZuIrSZIkSeo1E19JkiRJUq+Z+EqSJEmSei1VNekYlk2SHcBtk45jHg8B7px0EJoqtgnNZJvQTLYJzWSb0Ey2Cc3U1zbxg1V1yMzCVZX4rgRJNlXVhknHoelhm9BMtgnNZJvQTLYJzWSb0EyrrU3Y1VmSJEmS1GsmvpIkSZKkXjPxnT7nTDoATR3bhGayTWgm24Rmsk1oJtuEZlpVbcJnfCVJkiRJveYdX0mSJElSr5n4TokkT0tyU5Kbk5w56Xi0OEnek2R7ks0jZQcluTTJ1vZ5YCtPkre27/y6JMeOLHN6q781yekj5Y9Pcn1b5q1JsthtqHtJHpnkiiRbktyQ5BWt3DaxiiXZJ8nnklzb2sUftPIjklzZvrMPJFnbyvdu0ze3+YePrOusVn5Tkv86Uj72mLKYbWh5JNkzyReSfLRN2x5WuSS3tv37NUk2tTKPH6tYkgOSfDDJP2VwbnGcbWIXVJXDhAdgT+BLwJHAWuBa4OhJx+WwqO/yKcCxwOaRsjcBZ7bxM4E3tvFTgIuAAE8CrmzlBwG3tM8D2/iBbd7ngOPaMhcBT1/MNhyWrT2sA45t4/sBXwSOtk2s7qH92z+oje8FXNm+i78BXtDK/xz41Tb+a8Cft/EXAB9o40e348XewBHtOLLnXMeUXd2Gw7K2i98A3gd8dDHfle2hfwNwK/CQGWUeP1bxAJwPvLSNrwUOsE3swr/fpANwKFoDu3hk+izgrEnH5bDo7/NwHpj43gSsa+PrgJva+DuBF86sB7wQeOdI+Ttb2Trgn0bKd9bb1W1M+t9otQ7AR4CTbRMOI//+3wdcDfw4cCewppXvPC4AFwPHtfE1rV5mHiuG9WY7prRldmkbk/73WS0DcChwOXAi8NHFfFe2h/4NjE98PX6s0gHYH/jyzP+LtomFD3Z1ng6PAP55ZPr2VqZ+eFhVbQNonw9t5bN973OV3z6mfDHb0DJrXQWPYXB3zzaxyrVurdcA24FLGdyR+3pV3deqjH4vO7+zNv/fgIPZ9fZy8CK2oeXxJ8BvA/e36cV8V7aH/ingkiRXJTmjlXn8WL2OBHYA57XHIs5N8v3YJhbMxHc6ZEyZr9vuv9m+910tX8w2tIySPAj4EPDKqvrGXFXHlNkmeqiqvltV6xnc6Xsi8Nhx1drnUrWLub5728WEJPlZYHtVXTVaPKaq7WH1Ob6qjgWeDrwsyVPmqOvxo//WMHic7h1VdQzwbQbdjmdjm5jBxHc63A48cmT6UOCOCcWipffVJOsA2uf2Vj7b9z5X+aFjyhezDS2TJHsxSHovqKoPt2LbhACoqq8Dn2LwbNQBSda0WaPfy87vrM1/MHAXu95e7lzENtS944FnJLkV+GsG3Z3/BNvDqldVd7TP7cCFDC6SefxYvW4Hbq+qK9v0BxkkwraJBTLxnQ6fB47K4O2Kaxm8SGLjhGPS0tkInN7GT2fwnOew/MXtjXhPAv6tdR+5GPjpJAe2t+b9NIPnrrYB30zypPaWvRfPWNeubEPLoH1P7wa2VNUfj8yyTaxiSQ5JckAb3xc4CdgCXAGc1qrN/M6G3+VpwCdr8DDVRuAFGbyB9wjgKAYvJhl7TGnL7Oo21LGqOquqDq2qwxl8V5+sqhdhe1jVknx/kv2G4wz2+5vx+LFqVdVXgH9O8sOt6KnAjdgmFm7SDxk7DAYGb0X7IoPnvM6edDwOi/4e3w9sA+5lcBXslxk8F3U5sLV9HtTqBnhb+86vBzaMrOe/ATe34ZdGyjcwOPB9Cfgz2gsOFrMNh2VpD09m0OXnOuCaNpxim1jdA/A44AutXWwGfr+VH8kgUbkZ+Ftg71a+T5u+uc0/cmRdZ7fv8iba2zdb+dhjymK24bCsbeOn+M+3OtseVvHQvptr23DD8Hvz+LG6B2A9sKkdP/6OwVuZbRMLHIZ/jCRJkiRJvWRXZ0mSJElSr5n4SpIkSZJ6zcRXkiRJktRrJr6SJEmSpF4z8ZUkSZIk9ZqJryRJkiSp10x8JUkaI8nBSa5pw1eS/MvI9D90tM1jkpy7i8ucm+ToLuJZbklem+TVbfyPkpw46ZgkSf2wZtIBSJI0jarqa8B6GCRkwLeq6o863uzvAK/blQWq6qULrZtkTVXdt8tRTcb/Bt4FfHLSgUiSVj7v+EqStIuSfKt9/lSSTyf5myRfTPKGJC9K8rkk1yd5VKt3SJIPJfl8G44fs879gMdV1bVt+rVJzk9ySZJbkzw7yZvaej+RZK9W71NJNrTxpyW5Osm1SS4fWc85SS4B3ptknyTntfV8IckJrd6PtLivSXJdkqNa+S+MlL8zyZ5zbOugJH/Xlv9skseNxPCeFustSX595O8+O8lNSS4DfnhYXlW3AQcn+YGl/fYkSauRd3wlSdo9/wV4LHAXcAtwblU9MckrgP8BvBL4U+AtVfX3SQ4DLm7LjNoAbJ5R9ijgBOBo4B+B51TVbye5EPgZ4O+GFZMcwuAO6VOq6stJDhpZz+OBJ1fV3Ul+E6CqfizJY4BLkjwa+BXgT6vqgiRrgT2TPBZ4PnB8Vd2b5O3Ai5JcNMu2/gD4QlU9s3VTfi/trjnwmPa37AfclOQdwOOAFwDHMDgnuRq4aiTuq4HjgQ/N+q8vSdICmPhKkrR7Pl9V2wCSfAm4pJVfzyDRAzgJODrJcJn9k+xXVd8cWc86YMeMdV/UEs7rgT2BT4ys+/AZdZ8EfKaqvgxQVXeNzNtYVXe38Scz6EZMVf1TktuARzNIrM9Ocijw4aramuSpDJLmz7fY9wW2z7GtJwPPaWWfbM9JP7jN+1hV3QPck2Q78DDgJ4ALq+o77d9v44y/aTvwcCRJ2k0mvpIk7Z57RsbvH5m+n/88zu4BHDeSfI5zN7DPuHVX1f1J7q2qGrPuoQDFeN+eUe97VNX7klzJ4E7yxUle2uqeX1VnPWBDyTNm2da4dQ/rjf47fXck/tlihsG/x1z/ZpIkLYjP+EqS1L1LgJcPJ5KsH1NnC/BDu7GNfwR+MskRbRsHzVLvM8CLWp1HA4cx6Hp8JHBLVb0V2MigG/LlwGlJHjpcZ5IfnGNbo+v+KeDOqvrGHDF/BnhWkn3bM84/N2P+o/ne7t+SJO0y7/hKktS9XwfeluQ6BsfezzB4pnan1u34wWO6QC9IVe1Icgbw4SR7MOgmfPKYqm8H/rx1n74PeElV3ZPk+cAvJLkX+Arwv6rqriS/y+A54D2Ae4GXVdX5G6MPAAAAmklEQVRnZ9nWa4Hz2t/5HeD0eWK+OskHgGuA24D/M5zXXt71Q8CmXf23kCRppvxnrylJkjRJSV4FfLOqdum3fPsoybOAY6vq9yYdiyRp5bOrsyRJ0+MdPPBZ2NVsDfDmSQchSeoH7/hKkiRJknrNO76SJEmSpF4z8ZUkSZIk9ZqJryRJkiSp10x8JUmSJEm9ZuIrSZIkSeq1/w/voljAsIe4nAAAAABJRU5ErkJggg==n”, “text/plain”: [

“<Figure size 1152x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “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”, “print(timestamps)n”, “n”, “time1 = stream1.get_channel_sample_timestamps(channel_id,0,3000)n”, “print(“Time1”,time1)n”, “signal1 = stream1.get_channel_in_range(channel_id,0,3000)n”, “time2 = stream2.get_channel_sample_timestamps(channel_id,0,3000)n”, “print(“Time2”,time2)n”, “signal2 = stream2.get_channel_in_range(channel_id,0,3000)n”, “n”, “plt.figure(figsize=(16,6))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”, “[plt.axvline(timestamp, color=’r’) for timestamp in timestamps[0,:] if timestamp < max_time]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))”

]

}, {

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

“<a href=’#Top’>Back to index</a>”

]

}, {

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

“## Info <a id=’I2’></a>n”, “n”, “As depicted in the graphical representation of the class structure of the McsData.py module, every stream has an info file associated with it which holds additional information about the data included in the entities of those streams. This additional information can be used for simpler tasks like labeling axes with units stored in the info file or to sort streams according to parameters which are deposited in this kind of file.n”, “n”, “Below you find a collection of commands to access the data of the info files.”

]

}, {

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

“#### Accessing AnalogStream Info:”

]

}, {

“cell_type”: “code”, “execution_count”: 89, “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[0].channel_infos[0].info)”

]

}, {

“cell_type”: “code”, “execution_count”: 90, “metadata”: {}, “outputs”: [

{

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

“381470n”

]

}

], “source”: [

“print(channel_raw_data.recordings[0].analog_streams[0].channel_infos[0].info[‘ConversionFactor’])”

]

}, {

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

“As you can see the data is arranged in a dictionary, so we can get all the keys with .keys()”

]

}, {

“cell_type”: “code”, “execution_count”: 91, “metadata”: {}, “outputs”: [

{

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

“dict_keys([‘ChannelID’, ‘RowIndex’, ‘GroupID’, ‘Label’, ‘RawDataType’, ‘Unit’, ‘Exponent’, ‘ADZero’, ‘Tick’, ‘ConversionFactor’, ‘HighPassFilterType’, ‘HighPassFilterCutOffFrequency’, ‘HighPassFilterOrder’, ‘LowPassFilterType’, ‘LowPassFilterCutOffFrequency’, ‘LowPassFilterOrder’])n”

]

}

], “source”: [

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

]

}, {

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

“And we can use this list to iterate over it.”

]

}, {

“cell_type”: “code”, “execution_count”: 92, “metadata”: {}, “outputs”: [

{

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

“Key: ChannelID , Value: 0n”, “Key: RowIndex , Value: 0n”, “Key: GroupID , Value: 0n”, “Key: Label , Value: E1n”, “Key: RawDataType , Value: Intn”, “Key: Unit , Value: Vn”, “Key: Exponent , Value: -9n”, “Key: ADZero , Value: 0n”, “Key: Tick , Value: 2000n”, “Key: ConversionFactor , Value: 381470n”, “Key: HighPassFilterType , Value: n”, “Key: HighPassFilterCutOffFrequency , Value: -1n”, “Key: HighPassFilterOrder , Value: -1n”, “Key: LowPassFilterType , Value: n”, “Key: LowPassFilterCutOffFrequency , Value: -1n”, “Key: LowPassFilterOrder , Value: -1n”

]

}

], “source”: [

“for key in info_keys:n”, ” print(“Key:”,key,”, Value:”,channel_raw_data.recordings[0].analog_streams[0].channel_infos[0].info[key])”

]

}, {

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

“For AnalogStreams there are some built-in functions you can call on .channel_infos[index]. n”, “n”, “- .adc_stepn”, “- .channel_idn”, “- .row_indexn”, “- .versionn”

]

}, {

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

“If sampled data is enclosed in the AnalogStreamn”, “n”, “- .sampling_frequencyn”, “- .sampling_tickn”, “n”, “can be used.”

]

}, {

“cell_type”: “code”, “execution_count”: 93, “metadata”: {}, “outputs”: [

{

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

“500.0 hertzn”

]

}

], “source”: [

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

]

}, {

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

“For Entities .info.info reveals additional info.”

]

}, {

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

“#### Accessing FrameStream Info:”

]

}, {

“cell_type”: “code”, “execution_count”: 94, “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”: “code”, “execution_count”: 95, “metadata”: {}, “outputs”: [

{

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

“Shortn”

]

}

], “source”: [

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

]

}, {

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

“In addition there are:n”, “n”, “- .info.frame_idn”, “- .info.sensor_spacingn”, “- .info.adc_basic_stepn”, “- .info.adc_step_for_sensor(x, y) x,y are the coordinates of the sensor. Sensor 1 has (0, 0) sensor 4225 has (64, 64)”

]

}, {

“cell_type”: “code”, “execution_count”: 96, “metadata”: {}, “outputs”: [

{

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

“1e-09 voltn”

]

}

], “source”: [

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

]

}, {

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

“#### Accessing EventStream Info:”

]

}, {

“cell_type”: “code”, “execution_count”: 97, “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_raw_data.recordings[0].event_streams[0].event_entity[0].info.info)”

]

}, {

“cell_type”: “code”, “execution_count”: 98, “metadata”: {}, “outputs”: [

{

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

“8n”

]

}

], “source”: [

“print(event_raw_data.recordings[0].event_streams[0].event_entity[0].info.info[‘SourceChannelIDs’])”

]

}, {

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

“For EventStreams additionaly there are n”, “n”, “- .info.idn”, “- .info.raw_data_bytesn”, “- .info.source_channel_idsn”, “- .info.source_channel_labelsn”, “- .info.version “

]

}, {

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

“#### Accessing SegmentStream Info:”

]

}, {

“cell_type”: “code”, “execution_count”: 99, “metadata”: {}, “outputs”: [

{

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

“{‘SegmentID’: 31, ‘GroupID’: 2, ‘Label’: ‘Avg_31’, ‘PreInterval’: 10000, ‘PostInterval’: 500000, ‘SegmentType’: ‘Average’, ‘SourceChannelIDs’: ‘31’}n”

]

}

], “source”: [

“print(average_raw_data.recordings[0].segment_streams[0].segment_entity[31].info.info)”

]

}, {

“cell_type”: “code”, “execution_count”: 100, “metadata”: {}, “outputs”: [

{

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

“Avg_31n”

]

}

], “source”: [

“print(average_raw_data.recordings[0].segment_streams[0].segment_entity[31].info.label)”

]

}, {

“cell_type”: “code”, “execution_count”: 101, “metadata”: {}, “outputs”: [

{

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

“Avg_31n”

]

}

], “source”: [

“print(average_raw_data.recordings[0].segment_streams[0].segment_entity[31].info.info[‘Label’])”

]

}, {

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

“For Segments there also are:n”, “n”, “- .info.countn”, “- .info.idn”, “- .info.post_intervaln”, “- .info.pre_intervaln”, “- .info.typen”, “- .info.versionn”, “n”, “This holds true for cutout and average data alike.n”, “n”, “For entities with averages you can usen”, “n”, “- .number_of_averagesn”, “- .sample_lengthn”, “- .time_rangesn”, “- .time_range(index)n”, “- .average_countsn”, “- .average_count(index)n”, “- .segment_sample_count”

]

}, {

“cell_type”: “code”, “execution_count”: 102, “metadata”: {}, “outputs”: [

{

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

“9n”

]

}

], “source”: [

“print(average_raw_data.recordings[0].segment_streams[0].segment_entity[31].number_of_averages)”

]

}, {

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

“#### Accessing TimestampStream Info:”

]

}, {

“cell_type”: “code”, “execution_count”: 103, “metadata”: {}, “outputs”: [

{

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

“{‘TimeStampEntityID’: 0, ‘GroupID’: 0, ‘Label’: ‘’, ‘Unit’: ‘s’, ‘Exponent’: -6, ‘SourceChannelIDs’: ‘0’, ‘SourceChannelLabels’: ‘E1 \r\n’}n”

]

}

], “source”: [

“print(timestamps_raw_data.recordings[0].timestamp_streams[0].timestamp_entity[0].info.info)”

]

}, {

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

“And last but not least Timestamps have: n”, “n”, “- .info.data_typen”, “- .info.exponentn”, “- .info.idn”, “- .info.measuring_unitn”, “- .info.source_channel_idsn”, “- .info.source_channel_labelsn”, “- .info.unitn”, “- .info.version”

]

}, {

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

“<a href=’#Top’>Back to index</a>”

]

}

], “metadata”: {

“kernelspec”: {

“display_name”: “Python 3”, “language”: “python”, “name”: “python3”

}, “language_info”: {

“codemirror_mode”: {

“name”: “ipython”, “version”: 3

}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.7.0”

}

}, “nbformat”: 4, “nbformat_minor”: 2

}

{

“cells”: [

{

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“McsPyDataTools Tutorial for CMOS-MEA files<a id=’Top’></a>n”, “=======================n”, “n”, “This tutorial focuses on the HDF5 files generated by the MCS CMOS-MEA software (V. 2.0 and newer) and the usage of the McsPyDataTools toolbox to interact with these files.n”, “n”, “- ### <a href=’#McsPy’>The McsPy module for MCS CMOS-MEA file handling</a>n”, “- ### <a href=’#Mcs-HDF5’>Structure of the CMOS-MEA HDF5 files</a>n”, “n”, “- ### <a href=’#McsData Module’>McsData Classes and Inheritance</a>n”, “——————————————————————————————-n”, ” n”, “- ### <a href=’#Accessing your Data with McsData’>Accessing your Data with McsData</a>n”, ” - #### <a href=’#Req’>Requirements</a>n”, ” - #### <a href=’#naming’>Naming</a>n”, “- ### <a href=’#RawData’>Raw Data Files (.cmcr)</a>n”, ” - #### <a href=’#acquisition’>Acquisition</a>n”, ” - #### <a href=’#channelStream’>ChannelStream</a>n”, ” - #### <a href=’#sensorStream’>SensorStream</a>n”, ” - #### <a href=’#eventStream’>EventStream</a>n”, “- ### <a href=’#processedData’>Processed Data Files (.cmtr)</a>n”, ” - #### <a href=’#filterTool’>Filter Tool</a>n”, ” - #### <a href=’#spikeExplorer’>Spike Explorer</a>n”, ” - #### <a href=’#networkExplorer’>Network Explorer</a>n”, ” - #### <a href=’#spikeSorter’>Spike Sorter</a>n”, “n”, “The McsPy module for MCS CMOS-MEA file handling<a id=’McsPy’></a>n”, “—————————————————n”, “With the `h5py` package, a powerful tool for accessing HDF5 files in python already exists. This toolbox builds upon h5py by subclassing its central `h5py.Group` and `h5py.Dataset` classes as `McsGroup` and `McsDataset` respectively. Thus the McsPy classes feature all attributes and methods you might be used to from working with `h5py`, and simply extend them with MCS specific features. So if you are new to HDF5 in python you can always refer to the h5py documentation and discussions. Likewise, if you have worked with `h5py` previously you will find yourself in an at least familiar environment.n”, “If you prefer to work with h5py functionalities at any point in your analysis, feel free to retrieve the h5py base object from the McsPy object attribute `.h5py_object`n”, “n”, “`python\n", "    h5py_object = self.h5py_object\n", "`n”, “n”, “Structure of the HDF5 MCS-CMOS-MEA file system<a id=’Mcs-HDF5’></a>n”, “—————————————————n”, “n”, “A MCS-CMOS-MEA filesystem typically consists of two seperate files. A MCS CMOS-MEA RawData (RD) HDF5 file and a MCS CMOS-MEA ProcessedData (PD) HDF5 file.n”, “The RD file holds all raw data generated in a CMOS-MEA experiment with the CMOS-MEA-Control software, i.e. mainly different MCS data streams. The corresponding file extension is ‘.cmcr’.n”, “The PD file contains all data generated in post-processing raw data with the CMOS-MEA-Tools software. Each tool in the CMOS-MEA-Tools software (Filter Tool, STA or Network Explorer, Spike Explorer, and Spike Sorter) stores its results and settings in its own subgroup. Furthermore, the CMOS-MEA-Tools make use of the HDF5 capabilities to mount HDF5 files into each other. In that sense, the PD file mounts the RD file into the “Acquisition” subgroup of its own hierarchy tree. Thus, given the link in the PD file correctly points to the RD file, this toolbox provides a set of intuitive access tools for both RD and PD via just the PD file. The corresponding file extension is ‘.cmtr’.n”, “n”, “As the McsPyDataTools toolbox works with the underlying, strictly hierarchical HDF5 structure, the starting point of every data exploration with the McsPyDataTools toolbox is the `McsData` object. As the docstrings of the class already imply, this class was designed to hold the information of a complete CMOS-MEA HDF5 file system.n”, “n”, “`python\n", "    data = McsCMOSMEA.McsData('path to your data')\n", "`n”, “n”, “We highly recommend the supplementary use of the HDF Group’s HDFView software to help visualize and understand the structure of HDF5 files. This makes accessing the data much easier.n”, “n”, “<a href=’#Top’>Back to index</a>n”, “n”, “McsData Classes and Inheritance <a id=’McsData Module’></a>n”, “—————————————————————————————n”, “n”, “Generally, the McsPyDataTools toolbox creates a structure that, upon navigation through the file, reflects the CMOS-MEA HDF5 file structure, e.g. just as the CMOS-MEA HDF5 file system holds raw data in the “Acquisition” subgroup, the McsData object has an attribute `data.Acquisition`. Therefore, you can refer to the following graphical representation of the CMOS-MEA HDF5 file hierarchy for easy navigation through the python objects.n”, “n”, “Note: The subgroups of the root will not be accessible if they do not exist in the loaded CMOS-MEA file system.n”, “n”, “<a id=’file_structure_graphic’></a>n”, “<img src=”./Cmos_Hierarchy_short.png”>n”, “n”, “Upon initialization with the path to your datan”, “n”, “`python\n", "    data = McsCMOSMEA.McsData('path to your 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 neccessary, as not only the way how MCS programs handle the HDF5 formatted files may change, but the file format itself can undergo changes.n”, “n”, “Afterwards all information about the data stored in the file is retrieved from the HDF5 attributes, decoded and saved in the attribute `.attributes` as a dictionary. The `.attributes` dictionary is created for every McsPy object.n”, “n”, “`python\n", "    data.attributes\n", "`n”, “n”, “An access request on one of the subgroups or datasets (i.e. Acquisition, Filter Tool, STA Explorer, Network Explorer, Spike Explorer, Spike Sorter) readies the respective data:n”, “n”, “`python\n", "    aquisition_data = data.Aquisition\n", "`n”, “n”, “This instantiates a new McsPy object holding the all data about the requested subgroup.n”, “n”, “<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“## Accessing your Data with McsData<a id=’Accessing your Data with McsData’></a>n”, “n”, “Now that the general structure of a HDF5 file and the McsPy package with its McsData class is clear, we can walk through some quick and easy examples of how to access and visualize your data.n”, “n”, “n”, “Navigation through McsPy objects implements two central concepts:n”, “1. Groups work like dictionaries or classes.n”, “2. Datasets work like numpy arrays.n”, “n”, “### Naming<a id=’naming’></a>n”, “n”, “Wherever possible, McsPy names instances and attributes as found in the CMOS-MEA HDF5 file system. However, some systematic substitutions in group, dataset, and attribute naming are necessary to ensure python compatibility:n”, “n”, “n”, “| HDF5 MCS-CMOS-MEA | McsPyDataTools toolbox |\n", "|———————–|----------------------------|n”, “| whitespace | _ |\n", "| . | _ |\n", "| , | _ |\n", "| @ | at |\n", "| ( | (character removed) |\n", "| ) | (character removed) |\n", "| : | (character removed) |”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“### Requirements <a id=’Req’></a>n”, “n”, “So let’s dig in and get an impression of working with MCS CMOS-MEA data.n”, “n”, “First some modules need to be imported:”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [], “source”: [

“# These are the imports of the McsData modulen”, “import sys, importlib, osn”, “n”, “import McsPyn”, “import McsPy.McsCMOSMEA as McsCMOSMEAn”, “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 Slider, AxesWidgetn”, “import matplotlib.animation as animationn”, “from IPython.display import HTMLn”, “# 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 & pandas os pandasn”, “import numpy as npn”, “import pandas as pdn”, “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”, “n”, “# IMPORT cv2 to write a videon”, “from cv2 import VideoWriter, VideoWriter_fourccn”, “n”, “#import widgetsn”, “from ipywidgets import *n”, “from ipywebrtc import VideoStreamn”, “n”, “# autoreload modulesn”, “%load_ext autoreloadn”, “%autoreload 2”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “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”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [], “source”: [

“path2TestData = r’..\McsPyDataTools\McsPy\tests\TestData’ # adjust this!”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “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”: [

“## Raw Data Files (.cmcr)<a id=’RawData’></a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“In order to access a raw data file with extension .cmcr, we need to initialize an instance of the McsData class from the McsData module with the path to the file:”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [], “source”: [

“path2TestDataFile1 = os.path.join(path2TestData, “V200-SensorRoi-3Aux-Dig-Stim2-DiginEvts-5kHz.cmcr”)n”, “data = McsCMOSMEA.McsData(path2TestDataFile1)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“Note: The `McsData` call actually determines the type of MCS HDF5 file system which you have called, and returns an instance of an appropriate class. Do not be confused if the return is not an instance of `McsData` as you have maybe expected.n”, “n”, “To check if we got access to the file we can simply print the object. This gives a rough overview of the contents of the CMOS MEA RawData, or CMOS MEA ProcessedData file. In general, all McsGroup objects provide some information about themselves and a table of all subgroups or datasets (which you can check against the file hierarchy image) upon printing. The table also provides the McsPy access name of all contents.”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

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

“<McsCMOSMEAData instance at 0x246d6401ef0>n”, “n”, “This object represents the Mcs CMOS MEA file:n”, “Filename: V200-SensorRoi-3Aux-Dig-Stim2-DiginEvts-5kHz.cmcrn”, “n”, “Date Program Version n”, “——————- ————————– ———-n”, “10.10.2017 10:08:46 CMOS-MEA-Control 2.0.0.0 n”, “n”, “n”, “Content:n”, “n”, “| Mcs Type | HDF5 name | McsPy name |\n", "===============================================================================\n", "Groups:\n", "| Acquisition | Acquisition | Acquisition |n”, “——————————————————————————-n”, “Datasets:n”, ” Nonen”, “n”

]

}

], “source”: [

“print(data)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“The root object holds information about the Date of the recording, the Program which was used as well as its Version.n”, “n”, “Feel free to browse the `self.attributes` on any instance of `McsGroup` or `McsDataset` for more detailed information. And finally, you can call `McsGroup.tree(self)` for an ‘indent-tree’ of the current HDF5 Group and all its descendants. Feel free to check the output against the file hierarchy image <a href=’#file_structure_graphic’>above</a>.n”, “n”, “Note: As file trees may become very large very quickly, you are advised to print the instance rather than the tree once you are familiar with the MCS HDF5 file system.”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

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

“/n”, ” Acquisition n”, ” Analog_Data n”, ” ChannelData_1 n”, ” ChannelMeta n”, ” Digital_Data n”, ” ChannelData_1 n”, ” ChannelMeta n”, ” Digital_Data_Events n”, ” EventData n”, ” EventMeta n”, ” STG_Sideband_Events n”, ” EventData n”, ” EventMeta n”, ” StimulationSites n”, ” STG_Waveform n”, ” ChannelData_1 n”, ” ChannelMeta n”, ” StimulationSites n”, ” Sensor_Data n”, ” SensorData_1_1 n”, ” SensorMeta n”, “n”

]

}

], “source”: [

“print(data.tree())”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“### Accessing Acquisition Data<a id=’acquisition’></a>n”, “n”, “From the table we see that we can access the RawData, which is stored in the ‘Acquisition’ group of the MCS HDF5 file by simply calling the acquisition attribute on our `data` instance. Let’s go ahead and get a glimpse of the raw data streams in the file.”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

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

“——————————————————————————-n”, “Parent Group: <class ‘McsPyDataTools.McsPy.McsCMOSMEA.Acquisition’ object at 0x246d64201d0>n”, “n”, “——————————————————————————-n”, “n”, “n”, “| Subtype | HDF5 name | McsPy name |\n", "===============================================================================\n", "ChannelStream\n", "| Auxiliary | Analog Data | Analog_Data |\n", "| Digital | Digital Data | Digital_Data |\n", "| StgWaveform | STG Waveform | STG_Waveform |\n", "-------------------------------------------------------------------------------\n", "EventStream\n", "| DigitalPort | Digital Data Events | Digital_Data_Events |\n", "| StgSideband | STG Sideband Events | STG_Sideband_Events |\n", "-------------------------------------------------------------------------------\n", "SensorStream\n", "| CMosSensor | Sensor Data | Sensor_Data |n”, “——————————————————————————-n”, “n”

]

}

], “source”: [

“print(data.Acquisition)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“#### Channel Streams<a id=’channelStream’></a>n”, “We can navigate further to start and work with some channel data.”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

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

“——————————————————————————-n”, “Parent Group: <class ‘McsPyDataTools.McsPy.McsCMOSMEA.McsChannelStream’ object at 0x246d64205c0>n”, “——————————————————————————-n”, “n”, “n”, “| Mcs Type | HDF5 name | McsPy name |\n", "===============================================================================\n", "Groups:\n", " None\n", "-------------------------------------------------------------------------------\n", "Datasets:\n", "| ChannelData | ChannelData 1 | ChannelData_1 |\n", "| ChannelMeta | ChannelMeta | ChannelMeta |\n", "| Sites | StimulationSites | StimulationSites |n”, “n”

]

}

], “source”: [

“print(data.Acquisition.STG_Waveform)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“The Channel Stream ‘Digital Data’ object contains the two data sets `ChannelData_1` and `ChannelMeta`. The objects we obtain upon access are not arrays, but subclasses of `h5py.Dataset`. However, ```h5py.Dataset```s can be accessed, sliced and manipulated just as numpy arrays. So ```h5py.Dataset```s have a shape, a size, and a data type. For more information on working with Datasets please refer to the h5py <a href=’https://readthedocs.org/projects/h5py/’>documentation</a>.”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

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

“Shape: (1, 10000)n”, “Size: 10000n”, “Type: int32n”

]

}

], “source”: [

“print(‘Shape:’.ljust(10)+str(data.Acquisition.STG_Waveform.ChannelData_1.shape))n”, “print(‘Size:’.ljust(10)+str(data.Acquisition.STG_Waveform.ChannelData_1.size))n”, “print(‘Type:’.ljust(10)+str(data.Acquisition.STG_Waveform.ChannelData_1.dtype))”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“Now, let’s go ahead and visual the signal of a channel we recorded:”

]

}, {

“cell_type”: “code”, “execution_count”: 9, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

“data”: {

“text/plain”: [

“Text(0.5, 1.0, ‘Signal recorded by Channel 2’)”

]

}, “execution_count”: 9, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 980.64x472.32 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“channeldata_1 = data.Acquisition.STG_Waveform.ChannelData_1n”, “exponent = data.Acquisition.STG_Waveform.ChannelData_1.Meta[‘Exponent’][0]n”, “channel_id = data.Acquisition.STG_Waveform.ChannelData_1.Meta[‘ChannelID’][0]n”, “n”, “plt.figure(figsize=(0.681*20, 0.328*20))n”, “plt.plot(channeldata_1[0,2000:3000])n”, “plt.ylabel(‘Voltage [Ve’+str(exponent)+’]’)n”, “plt.xlabel(‘Time in timesteps’)n”, “plt.title(‘Signal recorded by Channel ‘+str(channel_id), fontweight=’bold’)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“#### Sensor Streams<a id=’sensorStream’></a>n”, “Alternatively we can investigate a Sensor Stream.”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

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

“——————————————————————————-n”, “Parent Group: <class ‘McsPyDataTools.McsPy.McsCMOSMEA.McsSensorStream’ object at 0x246d6507400>n”, “——————————————————————————-n”, “n”, “n”, “| Mcs Type | HDF5 name | McsPy name |\n", "===============================================================================\n", "Groups:\n", " None\n", "-------------------------------------------------------------------------------\n", "Datasets:\n", "| SensorData | SensorData 1 1 | SensorData_1_1 |\n", "| SensorMeta | SensorMeta | SensorMeta |n”, “n”

]

}

], “source”: [

“print(data.Acquisition.Sensor_Data)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“To visualize the sensor data as greyscale images, we can create an animation in matplotlib and play it as a HTML video. You’ll need to have FFmpeg available for this to work.”

]

}, {

“cell_type”: “code”, “execution_count”: null, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [], “source”: [

“images = data.Acquisition.Sensor_Data.SensorData_1_1n”, “n”, “fig = plt.figure()n”, “n”, “im = plt.imshow(images[0], animated=True, cmap=’gray’)n”, “plt.title(‘Sensor Data 1 1’)n”, “plt.box(False)n”, “n”, “def updatefig(i):n”, ” global imagesn”, ” im.set_array(images[i])n”, ” return im,n”, “n”, “ani = animation.FuncAnimation(fig, updatefig, interval=50, blit=True)n”, “n”, “plt.close(ani._fig)n”, “n”, “# Call function to display the animationn”, “HTML(ani.to_html5_video())”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“We can also take the sensor data and create a simple slider to go through the blocks of sensor data in time:”

]

}, {

“cell_type”: “code”, “execution_count”: 13, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

“data”: {

“application/javascript”: [

“/* Put everything inside the global mpl namespace /n”, “window.mpl = {};n”, “n”, “n”, “mpl.get_websocket_type = function() {n”, ” if (typeof(WebSocket) !== ‘undefined’) {n”, ” return WebSocket;n”, ” } else if (typeof(MozWebSocket) !== ‘undefined’) {n”, ” return MozWebSocket;n”, ” } else {n”, ” alert(‘Your browser does not have WebSocket support. ‘ +n”, ” ‘Please try Chrome, Safari or Firefox ≥ 6. ‘ +n”, ” ‘Firefox 4 and 5 are also supported but you ‘ +n”, ” ‘have to enable WebSockets in about:config.’);n”, ” };n”, “}n”, “n”, “mpl.figure = function(figure_id, websocket, ondownload, parent_element) {n”, ” this.id = figure_id;n”, “n”, ” this.ws = websocket;n”, “n”, ” this.supports_binary = (this.ws.binaryType != undefined);n”, “n”, ” if (!this.supports_binary) {n”, ” var warnings = document.getElementById(“mpl-warnings”);n”, ” if (warnings) {n”, ” warnings.style.display = ‘block’;n”, ” warnings.textContent = (n”, ” “This browser does not support binary websocket messages. ” +n”, ” “Performance may be slow.”);n”, ” }n”, ” }n”, “n”, ” this.imageObj = new Image();n”, “n”, ” this.context = undefined;n”, ” this.message = undefined;n”, ” this.canvas = undefined;n”, ” this.rubberband_canvas = undefined;n”, ” this.rubberband_context = undefined;n”, ” this.format_dropdown = undefined;n”, “n”, ” this.image_mode = ‘full’;n”, “n”, ” this.root = $(‘<div/>’);n”, ” this._root_extra_style(this.root)n”, ” this.root.attr(‘style’, ‘display: inline-block’);n”, “n”, ” $(parent_element).append(this.root);n”, “n”, ” this._init_header(this);n”, ” this._init_canvas(this);n”, ” this._init_toolbar(this);n”, “n”, ” var fig = this;n”, “n”, ” this.waiting = false;n”, “n”, ” this.ws.onopen = function () {n”, ” fig.send_message(“supports_binary”, {value: fig.supports_binary});n”, ” fig.send_message(“send_image_mode”, {});n”, ” if (mpl.ratio != 1) {n”, ” fig.send_message(“set_dpi_ratio”, {‘dpi_ratio’: mpl.ratio});n”, ” }n”, ” fig.send_message(“refresh”, {});n”, ” }n”, “n”, ” this.imageObj.onload = function() {n”, ” if (fig.image_mode == ‘full’) {n”, ” // Full images could contain transparency (where diff imagesn”, ” // almost always do), so we need to clear the canvas so thatn”, ” // there is no ghosting.n”, ” fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);n”, ” }n”, ” fig.context.drawImage(fig.imageObj, 0, 0);n”, ” };n”, “n”, ” this.imageObj.onunload = function() {n”, ” fig.ws.close();n”, ” }n”, “n”, ” this.ws.onmessage = this._make_on_message_function(this);n”, “n”, ” this.ondownload = ondownload;n”, “}n”, “n”, “mpl.figure.prototype._init_header = function() {n”, ” var titlebar = $(n”, ” ‘<div class=”ui-dialog-titlebar ui-widget-header ui-corner-all ‘ +n”, ” ‘ui-helper-clearfix”/>’);n”, ” var titletext = $(n”, ” ‘<div class=”ui-dialog-title” style=”width: 100%; ‘ +n”, ” ‘text-align: center; padding: 3px;”/>’);n”, ” titlebar.append(titletext)n”, ” this.root.append(titlebar);n”, ” this.header = titletext[0];n”, “}n”, “n”, “n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “n”, “mpl.figure.prototype._root_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “mpl.figure.prototype._init_canvas = function() {n”, ” var fig = this;n”, “n”, ” var canvas_div = $(‘<div/>’);n”, “n”, ” canvas_div.attr(‘style’, ‘position: relative; clear: both; outline: 0’);n”, “n”, ” function canvas_keyboard_event(event) {n”, ” return fig.key_event(event, event[‘data’]);n”, ” }n”, “n”, ” canvas_div.keydown(‘key_press’, canvas_keyboard_event);n”, ” canvas_div.keyup(‘key_release’, canvas_keyboard_event);n”, ” this.canvas_div = canvas_divn”, ” this._canvas_extra_style(canvas_div)n”, ” this.root.append(canvas_div);n”, “n”, ” var canvas = $(‘<canvas/>’);n”, ” canvas.addClass(‘mpl-canvas’);n”, ” canvas.attr(‘style’, “left: 0; top: 0; z-index: 0; outline: 0”)n”, “n”, ” this.canvas = canvas[0];n”, ” this.context = canvas[0].getContext(“2d”);n”, “n”, ” var backingStore = this.context.backingStorePixelRatio ||n”, “tthis.context.webkitBackingStorePixelRatio ||n”, “tthis.context.mozBackingStorePixelRatio ||n”, “tthis.context.msBackingStorePixelRatio ||n”, “tthis.context.oBackingStorePixelRatio ||n”, “tthis.context.backingStorePixelRatio || 1;n”, “n”, ” mpl.ratio = (window.devicePixelRatio || 1) / backingStore;n”, “n”, ” var rubberband = $(‘<canvas/>’);n”, ” rubberband.attr(‘style’, “position: absolute; left: 0; top: 0; z-index: 1;”)n”, “n”, ” var pass_mouse_events = true;n”, “n”, ” canvas_div.resizable({n”, ” start: function(event, ui) {n”, ” pass_mouse_events = false;n”, ” },n”, ” resize: function(event, ui) {n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” stop: function(event, ui) {n”, ” pass_mouse_events = true;n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” });n”, “n”, ” function mouse_event_fn(event) {n”, ” if (pass_mouse_events)n”, ” return fig.mouse_event(event, event[‘data’]);n”, ” }n”, “n”, ” rubberband.mousedown(‘button_press’, mouse_event_fn);n”, ” rubberband.mouseup(‘button_release’, mouse_event_fn);n”, ” // Throttle sequential mouse events to 1 every 20ms.n”, ” rubberband.mousemove(‘motion_notify’, mouse_event_fn);n”, “n”, ” rubberband.mouseenter(‘figure_enter’, mouse_event_fn);n”, ” rubberband.mouseleave(‘figure_leave’, mouse_event_fn);n”, “n”, ” canvas_div.on(“wheel”, function (event) {n”, ” event = event.originalEvent;n”, ” event[‘data’] = ‘scroll’n”, ” if (event.deltaY < 0) {n”, ” event.step = 1;n”, ” } else {n”, ” event.step = -1;n”, ” }n”, ” mouse_event_fn(event);n”, ” });n”, “n”, ” canvas_div.append(canvas);n”, ” canvas_div.append(rubberband);n”, “n”, ” this.rubberband = rubberband;n”, ” this.rubberband_canvas = rubberband[0];n”, ” this.rubberband_context = rubberband[0].getContext(“2d”);n”, ” this.rubberband_context.strokeStyle = “#000000”;n”, “n”, ” this._resize_canvas = function(width, height) {n”, ” // Keep the size of the canvas, canvas container, and rubber bandn”, ” // canvas in synch.n”, ” canvas_div.css(‘width’, width)n”, ” canvas_div.css(‘height’, height)n”, “n”, ” canvas.attr(‘width’, width * mpl.ratio);n”, ” canvas.attr(‘height’, height * mpl.ratio);n”, ” canvas.attr(‘style’, ‘width: ‘ + width + ‘px; height: ‘ + height + ‘px;’);n”, “n”, ” rubberband.attr(‘width’, width);n”, ” rubberband.attr(‘height’, height);n”, ” }n”, “n”, ” // Set the figure to an initial 600x600px, this will subsequently be updatedn”, ” // upon first draw.n”, ” this._resize_canvas(600, 600);n”, “n”, ” // Disable right mouse context menu.n”, ” $(this.rubberband_canvas).bind(“contextmenu”,function(e){n”, ” return false;n”, ” });n”, “n”, ” function set_focus () {n”, ” canvas.focus();n”, ” canvas_div.focus();n”, ” }n”, “n”, ” window.setTimeout(set_focus, 100);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items) {n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) {n”, ” // put a spacer in here.n”, ” continue;n”, ” }n”, ” var button = $(‘<button/>’);n”, ” button.addClass(‘ui-button ui-widget ui-state-default ui-corner-all ‘ +n”, ” ‘ui-button-icon-only’);n”, ” button.attr(‘role’, ‘button’);n”, ” button.attr(‘aria-disabled’, ‘false’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, “n”, ” var icon_img = $(‘<span/>’);n”, ” icon_img.addClass(‘ui-button-icon-primary ui-icon’);n”, ” icon_img.addClass(image);n”, ” icon_img.addClass(‘ui-corner-all’);n”, “n”, ” var tooltip_span = $(‘<span/>’);n”, ” tooltip_span.addClass(‘ui-button-text’);n”, ” tooltip_span.html(tooltip);n”, “n”, ” button.append(icon_img);n”, ” button.append(tooltip_span);n”, “n”, ” nav_element.append(button);n”, ” }n”, “n”, ” var fmt_picker_span = $(‘<span/>’);n”, “n”, ” var fmt_picker = $(‘<select/>’);n”, ” fmt_picker.addClass(‘mpl-toolbar-option ui-widget ui-widget-content’);n”, ” fmt_picker_span.append(fmt_picker);n”, ” nav_element.append(fmt_picker_span);n”, ” this.format_dropdown = fmt_picker[0];n”, “n”, ” for (var ind in mpl.extensions) {n”, ” var fmt = mpl.extensions[ind];n”, ” var option = $(n”, ” ‘<option/>’, {selected: fmt === mpl.default_extension}).html(fmt);n”, ” fmt_picker.append(option);n”, ” }n”, “n”, ” // Add hover states to the ui-buttonsn”, ” $( “.ui-button” ).hover(n”, ” function() { $(this).addClass(“ui-state-hover”);},n”, ” function() { $(this).removeClass(“ui-state-hover”);}n”, ” );n”, “n”, ” var status_bar = $(‘<span class=”mpl-message”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “}n”, “n”, “mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {n”, ” // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,n”, ” // which will in turn request a refresh of the image.n”, ” this.send_message(‘resize’, {‘width’: x_pixels, ‘height’: y_pixels});n”, “}n”, “n”, “mpl.figure.prototype.send_message = function(type, properties) {n”, ” properties[‘type’] = type;n”, ” properties[‘figure_id’] = this.id;n”, ” this.ws.send(JSON.stringify(properties));n”, “}n”, “n”, “mpl.figure.prototype.send_draw_message = function() {n”, ” if (!this.waiting) {n”, ” this.waiting = true;n”, ” this.ws.send(JSON.stringify({type: “draw”, figure_id: this.id}));n”, ” }n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” var format_dropdown = fig.format_dropdown;n”, ” var format = format_dropdown.options[format_dropdown.selectedIndex].value;n”, ” fig.ondownload(fig, format);n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_resize = function(fig, msg) {n”, ” var size = msg[‘size’];n”, ” if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {n”, ” fig._resize_canvas(size[0], size[1]);n”, ” fig.send_message(“refresh”, {});n”, ” };n”, “}n”, “n”, “mpl.figure.prototype.handle_rubberband = function(fig, msg) {n”, ” var x0 = msg[‘x0’] / mpl.ratio;n”, ” var y0 = (fig.canvas.height - msg[‘y0’]) / mpl.ratio;n”, ” var x1 = msg[‘x1’] / mpl.ratio;n”, ” var y1 = (fig.canvas.height - msg[‘y1’]) / mpl.ratio;n”, ” x0 = Math.floor(x0) + 0.5;n”, ” y0 = Math.floor(y0) + 0.5;n”, ” x1 = Math.floor(x1) + 0.5;n”, ” y1 = Math.floor(y1) + 0.5;n”, ” var min_x = Math.min(x0, x1);n”, ” var min_y = Math.min(y0, y1);n”, ” var width = Math.abs(x1 - x0);n”, ” var height = Math.abs(y1 - y0);n”, “n”, ” fig.rubberband_context.clearRect(n”, ” 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);n”, “n”, ” fig.rubberband_context.strokeRect(min_x, min_y, width, height);n”, “}n”, “n”, “mpl.figure.prototype.handle_figure_label = function(fig, msg) {n”, ” // Updates the figure title.n”, ” fig.header.textContent = msg[‘label’];n”, “}n”, “n”, “mpl.figure.prototype.handle_cursor = function(fig, msg) {n”, ” var cursor = msg[‘cursor’];n”, ” switch(cursor)n”, ” {n”, ” case 0:n”, ” cursor = ‘pointer’;n”, ” break;n”, ” case 1:n”, ” cursor = ‘default’;n”, ” break;n”, ” case 2:n”, ” cursor = ‘crosshair’;n”, ” break;n”, ” case 3:n”, ” cursor = ‘move’;n”, ” break;n”, ” }n”, ” fig.rubberband_canvas.style.cursor = cursor;n”, “}n”, “n”, “mpl.figure.prototype.handle_message = function(fig, msg) {n”, ” fig.message.textContent = msg[‘message’];n”, “}n”, “n”, “mpl.figure.prototype.handle_draw = function(fig, msg) {n”, ” // Request the server to send over a new figure.n”, ” fig.send_draw_message();n”, “}n”, “n”, “mpl.figure.prototype.handle_image_mode = function(fig, msg) {n”, ” fig.image_mode = msg[‘mode’];n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Called whenever the canvas gets updated.n”, ” this.send_message(“ack”, {});n”, “}n”, “n”, “// A function to construct a web socket function for onmessage handling.n”, “// Called in the figure constructor.n”, “mpl.figure.prototype._make_on_message_function = function(fig) {n”, ” return function socket_on_message(evt) {n”, ” if (evt.data instanceof Blob) {n”, ” / FIXME: We get “Resource interpreted as Image butn”, ” * transferred with MIME type text/plain:” errors onn”, ” * Chrome. But how to set the MIME type? It doesn’t seemn”, ” * to be part of the websocket stream /n”, ” evt.data.type = “image/png”;n”, “n”, ” / Free the memory for the previous frames /n”, ” if (fig.imageObj.src) {n”, ” (window.URL || window.webkitURL).revokeObjectURL(n”, ” fig.imageObj.src);n”, ” }n”, “n”, ” fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(n”, ” evt.data);n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, ” else if (typeof evt.data === ‘string’ && evt.data.slice(0, 21) == “data:image/png;base64”) {n”, ” fig.imageObj.src = evt.data;n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, “n”, ” var msg = JSON.parse(evt.data);n”, ” var msg_type = msg[‘type’];n”, “n”, ” // Call the “handle_{type}” callback, which takesn”, ” // the figure and JSON message as its only arguments.n”, ” try {n”, ” var callback = fig[“handle_” + msg_type];n”, ” } catch (e) {n”, ” console.log(“No handler for the ‘” + msg_type + “’ message type: “, msg);n”, ” return;n”, ” }n”, “n”, ” if (callback) {n”, ” try {n”, ” // console.log(“Handling ‘” + msg_type + “’ message: “, msg);n”, ” callback(fig, msg);n”, ” } catch (e) {n”, ” console.log(“Exception inside the ‘handler_” + msg_type + “’ callback:”, e, e.stack, msg);n”, ” }n”, ” }n”, ” };n”, “}n”, “n”, “// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvasn”, “mpl.findpos = function(e) {n”, ” //this section is from http://www.quirksmode.org/js/events_properties.htmln”, ” var targ;n”, ” if (!e)n”, ” e = window.event;n”, ” if (e.target)n”, ” targ = e.target;n”, ” else if (e.srcElement)n”, ” targ = e.srcElement;n”, ” if (targ.nodeType == 3) // defeat Safari bugn”, ” targ = targ.parentNode;n”, “n”, ” // jQuery normalizes the pageX and pageYn”, ” // pageX,Y are the mouse positions relative to the documentn”, ” // offset() returns the position of the element relative to the documentn”, ” var x = e.pageX - $(targ).offset().left;n”, ” var y = e.pageY - $(targ).offset().top;n”, “n”, ” return {“x”: x, “y”: y};n”, “};n”, “n”, “/n”, ” * return a copy of an object with only non-object keysn”, ” * we need this to avoid circular referencesn”, ” * http://stackoverflow.com/a/24161582/3208463n”, ” /n”, “function simpleKeys (original) {n”, ” return Object.keys(original).reduce(function (obj, key) {n”, ” if (typeof original[key] !== ‘object’)n”, ” obj[key] = original[key]n”, ” return obj;n”, ” }, {});n”, “}n”, “n”, “mpl.figure.prototype.mouse_event = function(event, name) {n”, ” var canvas_pos = mpl.findpos(event)n”, “n”, ” if (name === ‘button_press’)n”, ” {n”, ” this.canvas.focus();n”, ” this.canvas_div.focus();n”, ” }n”, “n”, ” var x = canvas_pos.x * mpl.ratio;n”, ” var y = canvas_pos.y * mpl.ratio;n”, “n”, ” this.send_message(name, {x: x, y: y, button: event.button,n”, ” step: event.step,n”, ” guiEvent: simpleKeys(event)});n”, “n”, ” / This prevents the web browser from automatically changing ton”, ” * the text insertion cursor when the button is pressed. We wantn”, ” * to control all of the cursor setting manually through then”, ” * ‘cursor’ event from matplotlib /n”, ” event.preventDefault();n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” // Handle any extra behaviour associated with a key eventn”, “}n”, “n”, “mpl.figure.prototype.key_event = function(event, name) {n”, “n”, ” // Prevent repeat eventsn”, ” if (name == ‘key_press’)n”, ” {n”, ” if (event.which === this._key)n”, ” return;n”, ” elsen”, ” this._key = event.which;n”, ” }n”, ” if (name == ‘key_release’)n”, ” this._key = null;n”, “n”, ” var value = ‘’;n”, ” if (event.ctrlKey && event.which != 17)n”, ” value += “ctrl+”;n”, ” if (event.altKey && event.which != 18)n”, ” value += “alt+”;n”, ” if (event.shiftKey && event.which != 16)n”, ” value += “shift+”;n”, “n”, ” value += ‘k’;n”, ” value += event.which.toString();n”, “n”, ” this._key_event_extra(event, name);n”, “n”, ” this.send_message(name, {key: value,n”, ” guiEvent: simpleKeys(event)});n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype.toolbar_button_onclick = function(name) {n”, ” if (name == ‘download’) {n”, ” this.handle_save(this, null);n”, ” } else {n”, ” this.send_message(“toolbar_button”, {name: name});n”, ” }n”, “};n”, “n”, “mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {n”, ” this.message.textContent = tooltip;n”, “};n”, “mpl.toolbar_items = [[“Home”, “Reset original view”, “fa fa-home icon-home”, “home”], [“Back”, “Back to previous view”, “fa fa-arrow-left icon-arrow-left”, “back”], [“Forward”, “Forward to next view”, “fa fa-arrow-right icon-arrow-right”, “forward”], [“”, “”, “”, “”], [“Pan”, “Pan axes with left mouse, zoom with right”, “fa fa-arrows icon-move”, “pan”], [“Zoom”, “Zoom to rectangle”, “fa fa-square-o icon-check-empty”, “zoom”], [“”, “”, “”, “”], [“Download”, “Download plot”, “fa fa-floppy-o icon-save”, “download”]];n”, “n”, “mpl.extensions = [“eps”, “jpeg”, “pdf”, “png”, “ps”, “raw”, “svg”, “tif”];n”, “n”, “mpl.default_extension = “png”;var comm_websocket_adapter = function(comm) {n”, ” // Create a “websocket”-like object which calls the given IPython commn”, ” // object with the appropriate methods. Currently this is a non binaryn”, ” // socket, so there is still some room for performance tuning.n”, ” var ws = {};n”, “n”, ” ws.close = function() {n”, ” comm.close()n”, ” };n”, ” ws.send = function(m) {n”, ” //console.log(‘sending’, m);n”, ” comm.send(m);n”, ” };n”, ” // Register the callback with on_msg.n”, ” comm.on_msg(function(msg) {n”, ” //console.log(‘receiving’, msg[‘content’][‘data’], msg);n”, ” // Pass the mpl event to the overridden (by mpl) onmessage function.n”, ” ws.onmessage(msg[‘content’][‘data’])n”, ” });n”, ” return ws;n”, “}n”, “n”, “mpl.mpl_figure_comm = function(comm, msg) {n”, ” // This is the function which gets called when the mpl processn”, ” // starts-up an IPython Comm through the “matplotlib” channel.n”, “n”, ” var id = msg.content.data.id;n”, ” // Get hold of the div created by the display call when the Commn”, ” // socket was opened in Python.n”, ” var element = $(“#” + id);n”, ” var ws_proxy = comm_websocket_adapter(comm)n”, “n”, ” function ondownload(figure, format) {n”, ” window.open(figure.imageObj.src);n”, ” }n”, “n”, ” var fig = new mpl.figure(id, ws_proxy,n”, ” ondownload,n”, ” element.get(0));n”, “n”, ” // Call onopen now - mpl needs it, as it is assuming we’ve passed it a realn”, ” // web socket which is closed, not our websocket->open comm proxy.n”, ” ws_proxy.onopen();n”, “n”, ” fig.parent_element = element.get(0);n”, ” fig.cell_info = mpl.find_output_cell(“<div id=’” + id + “’></div>”);n”, ” if (!fig.cell_info) {n”, ” console.error(“Failed to find cell for figure”, id, fig);n”, ” return;n”, ” }n”, “n”, ” var output_index = fig.cell_info[2]n”, ” var cell = fig.cell_info[0];n”, “n”, “};n”, “n”, “mpl.figure.prototype.handle_close = function(fig, msg) {n”, ” var width = fig.canvas.width/mpl.ration”, ” fig.root.unbind(‘remove’)n”, “n”, ” // Update the output cell to use the data from the current canvas.n”, ” fig.push_to_output();n”, ” var dataURL = fig.canvas.toDataURL();n”, ” // Re-enable the keyboard manager in IPython - without this line, in FF,n”, ” // the notebook keyboard shortcuts fail.n”, ” IPython.keyboard_manager.enable()n”, ” $(fig.parent_element).html(‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’);n”, ” fig.close_ws(fig, msg);n”, “}n”, “n”, “mpl.figure.prototype.close_ws = function(fig, msg){n”, ” fig.send_message(‘closing’, msg);n”, ” // fig.ws.close()n”, “}n”, “n”, “mpl.figure.prototype.push_to_output = function(remove_interactive) {n”, ” // Turn the data on the canvas into data in the output cell.n”, ” var width = this.canvas.width/mpl.ration”, ” var dataURL = this.canvas.toDataURL();n”, ” this.cell_info[1][‘text/html’] = ‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’;n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Tell IPython that the notebook contents must change.n”, ” IPython.notebook.set_dirty(true);n”, ” this.send_message(“ack”, {});n”, ” var fig = this;n”, ” // Wait a second, then push the new image to the DOM son”, ” // that it is saved nicely (might be nice to debounce this).n”, ” setTimeout(function () { fig.push_to_output() }, 1000);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items){n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) { continue; };n”, “n”, ” var button = $(‘<button class=”btn btn-default” href=”#” title=”’ + name + ‘”><i class=”fa ‘ + image + ‘ fa-lg”></i></button>’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, ” nav_element.append(button);n”, ” }n”, “n”, ” // Add the status bar.n”, ” var status_bar = $(‘<span class=”mpl-message” style=”text-align:right; float: right;”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “n”, ” // Add the close button to the window.n”, ” var buttongrp = $(‘<div class=”btn-group inline pull-right”></div>’);n”, ” var button = $(‘<button class=”btn btn-mini btn-primary” href=”#” title=”Stop Interaction”><i class=”fa fa-power-off icon-remove icon-large”></i></button>’);n”, ” button.click(function (evt) { fig.handle_close(fig, {}); } );n”, ” button.mouseover(‘Stop Interaction’, toolbar_mouse_event);n”, ” buttongrp.append(button);n”, ” var titlebar = this.root.find($(‘.ui-dialog-titlebar’));n”, ” titlebar.prepend(buttongrp);n”, “}n”, “n”, “mpl.figure.prototype._root_extra_style = function(el){n”, ” var fig = thisn”, ” el.on(“remove”, function(){n”, “tfig.close_ws(fig, {});n”, ” });n”, “}n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(el){n”, ” // this is important to make the div ‘focusablen”, ” el.attr(‘tabindex’, 0)n”, ” // reach out to IPython and tell the keyboard manager to turn it’s selfn”, ” // off when our div gets focusn”, “n”, ” // location in version 3n”, ” if (IPython.notebook.keyboard_manager) {n”, ” IPython.notebook.keyboard_manager.register_events(el);n”, ” }n”, ” else {n”, ” // location in version 2n”, ” IPython.keyboard_manager.register_events(el);n”, ” }n”, “n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” var manager = IPython.notebook.keyboard_manager;n”, ” if (!manager)n”, ” manager = IPython.keyboard_manager;n”, “n”, ” // Check for shift+entern”, ” if (event.shiftKey && event.which == 13) {n”, ” this.canvas_div.blur();n”, ” event.shiftKey = false;n”, ” // Send a “J” for go to next celln”, ” event.which = 74;n”, ” event.keyCode = 74;n”, ” manager.command_mode();n”, ” manager.handle_keydown(event);n”, ” }n”, “}n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” fig.ondownload(fig, null);n”, “}n”, “n”, “n”, “mpl.find_output_cell = function(html_output) {n”, ” // Return the cell and output element which can be found *uniquely in the notebook.n”, ” // Note - this is a bit hacky, but it is done because the “notebook_saving.Notebook”n”, ” // IPython event is triggered only after the cells have been serialised, which forn”, ” // our purposes (turning an active figure into a static one), is too late.n”, ” var cells = IPython.notebook.get_cells();n”, ” var ncells = cells.length;n”, ” for (var i=0; i<ncells; i++) {n”, ” var cell = cells[i];n”, ” if (cell.cell_type === ‘code’){n”, ” for (var j=0; j<cell.output_area.outputs.length; j++) {n”, ” var data = cell.output_area.outputs[j];n”, ” if (data.data) {n”, ” // IPython >= 3 moved mimebundle to data attribute of outputn”, ” data = data.data;n”, ” }n”, ” if (data[‘text/html’] == html_output) {n”, ” return [cell, data, j];n”, ” }n”, ” }n”, ” }n”, ” }n”, “}n”, “n”, “// Register the function which deals with the matplotlib target/channel.n”, “// The kernel may be null if the page has been refreshed.n”, “if (IPython.notebook.kernel != null) {n”, ” IPython.notebook.kernel.comm_manager.register_target(‘matplotlib’, mpl.mpl_figure_comm);n”, “}n”

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f0tSc9JiiHtz0j6bJ1IgBpQA2pAzdB3s9ECQD0Icgd7puugPltSbLfzOkn3SXqfpBO3O7pGUvzTVVddNeeiiy7qUZI56lQS5qhLPZijbtbxMEed6sQcdaqHO6+cOeoXdeo6qKvW8FFJsR/nJ1zXgkeNR41HjUeNR41H3ezn58lL1WVQ/4ykVxRR3/H/w6OOzXr/2Ml77bXXZqO+3Q48bts7F3hU16xuLtC920XwukAO9+66vcddh+WCzA4fjti93stFg5977rnZ9C4wJxK78rpIavduN9/o9Haes/OMoqzu8BK3P7grk9Nj/vz5Wf1cpHtdW7vgMLeXtItEd/tzu/226/akvueee7L1c9Hd7h1u/3FnG24v8bqoedd2bfsCt1e/s2/3zV1xxRW2uxlWH9H2B42zyyjo8uXLs+V1urqVIRs3buwk0zpZqaLF3yxp7Et/paQvSLq5DpaAOlUHUPcf4gbUqc0A6lQPQF3qAagH98C7DOrWqgBqQI1HndoAHnWpBx51s/7BecGAujWS/uYBQF3RDlA3+xAZ+i51wqPGow4FGPou7QBQDw5k9ySgBtTWqhj6ZugbjxqP2sU2MEc9fCAD6gaa4lHjUTP0zdA3wWSpDQDqBvAY5yR41BWBt2zZko36dlGSLvKwbq9vF13rIlyPHIll4L3XlVdemb3v1iO6COQ6+3IRpW5NrLu/d+/e7Gtc1LLbSCEycXsbn3HGGdl3uLZz+0ivXbs2m4/bs9lFFEcmrq1nz56dfYezG7dn+IoVK7L5uLnUAwcO2OZevXp19m/Oblwd3LCnCzJzkf9RGGdPDz30ULass2bNyt6fNm1a9n7du3MP1Nmly8u1hYOfG8F49NFHs3V49atfnb3vDhap+95dJL+LOHe24VZIbN++3b7e2Y1bIXHLLbdk8/rmN7/ZSaZ1slKD/rgB1KlygLrUA1CntgGoUz0AdakHoB6UQP45QF3RBlADajzq1AbwqEs98Kib2QagBtTDVwBQW03xqPGoATWgZuh7XLHTKHM8akANqCsK4FE385oY+mbomznqRowdSiJADagBNaAWwWT9+1OGvpv9iGPou78ttU0BqCuK7d27Nxv17T7QQc7qdVGgLpLVeS4uorht5Kbb7zhkcXm59ZMf/vCHs/Z32WWXZe+7yFdXt8jkrrvuyublouBdFK17t9vMZenSpdn3unaLxG6Pbhfh6qLgXXoXFe2GKp0tRVndNIdbrrZ79+6sHi7ozn0rLho8Mnd7d7tOzkX4u208XXqna13n6iLq3coQBzPnpbpv0dnfpk2bbHFd9LWzs7rvMfcSp3ddPsePH8+W9957783eX7BgQfb+unXrOsm0Tlaq7a+VsfSAOlUOUJd6AOrUNgB1qgegLvUA1IMSyD8HqPGorXUAakCNR92s0wXUgLqZpQyWClADakBdUYCh79QcAHWzjhVQA+pmljJYKkANqAE1oGaOumIDzFGnXQJz1IPBdZhPAWpADagBNaAG1PYEMEA9TOQOlhegBtSAGlADakANqAdj6El5ClBXZB4dHc0uz3IHL5xzzjmtG8ktkXnggQeyeT333HPZ+25vYTds55bH1A3ztR0CXLx4cbasmzdvzt5vm39k0najDddAbsld2wC60dFRawPuYIT9+/dnn3HLudwmLG7pj1uy4+oWhXHvdm3klhm6Of62B5REmdwyM5eXW27VdpmXO7DFLamKsm7YsCHbpm59ujMa9w537rlbXunaId7rlkK5aG1nG26J4wUXXJCtnrPXSOwOH3IxEk6/G264oZNM62SlWtOzeABQp8q1BSmgTvUD1KUegLpZrwSoU50A9Yt6AGo8atuDAOpSGrfJCx51aj541KkeeNSlHnjUzX6s5VIBakANqCsKMPTdbFSFoe9UJ4a+Sz0Y+h4cyO5JQA2oATWgZo66YgPMUaddAnPUwwdv2xwBNaAG1IAaUANqEUzWFp8nL30XQP15SUskfTeCggvpTpe0U9KMOG9A0rsk5cOnK1qPjIxko75dxLSbj3OHK8SrLrnkkmzr3nPPPdn7LuJ8zZo12fRtN9avC9bYu3dv9h0u2tPN47q6OV3dQRpRGBdp7yJ7XXS8i/res2dPts633npr9n5ddK0LoLrvvvuyebVNv2RJmH3v5aJ6d+6MTyJ/uXdfffXV2Qcefvjh7P3TT49Pr/dyetedSHXo0KFsXueff372ftvIdRcl7u7XrfJw86/OPpxObkrBRXe7aHB3OEoI54Ic3XftyuSmidy3WHcAiyuvs2XX1suXL+8C03rsuwuVulDSMUmxTmYM1LEe6KikWyStl/RaSev6/f4B1KlCgLrUA1CntgGoUz0AdakHoO5HmvZ/7wKoo9bhOcd5aGOgjp/icQ7aU5LOkhSLlGf2kwdQA2o86tQG8KhLPfCoU9vAo+5HlOH9vaug/r6k11RkimHv8KprL0ANqAE1oGboO7UBhr77kWP8/w6opWv04j8tW7Zszty5c3tUZ446lYQ56lIP5qhT22COOtWDOepSD+aoBwd6V0HN0PcJNuGChQgmS4XCo8ajxqPGox4cqePzZFdB/XFJz1aCySIU9cZ+El599dXZqG8HM/dB1/1ydFHI7pkLL4xYueaXK5OLpF66dKnNfNu2bdm/ubmpZcuWZdO7va2nTZuWTe8i1yOxa4tZs2Zl83IR564OZ599djYf90MnNwIzloHb49y92+0E13aHOKfRrl27bFu7+rklO66NnJ251Q51lu12fXOrKpw3/8QTT2Rf44Ke3FCv+3YjcxdM5kaf3N7+b3rTm7JldXVz73XpI3PXpq7vcOndSKOL4N6xY4dtbhfV7trC9Yvs9d2cFSc75V1F4NgZkr4j6SOSviTpi5LeKOlJSe8sosBrywaoU3kAdakHoE5tA1CnegDqUg9APXwEdsWjHooygBpQ41GnNoBHXeqBR53aBh71ULDTKBNAXZEJUANqQA2oGfpuZgOAuhFjh5IIUANqa0gMfTP0jUeNR80c9VBY+5IyAdSAGlBXFMCjbuZNMUfNHDUe9Utib6uHAXVFrl27dmWjvl3k8OzZYxuhpZofPhzbi+evlStXZv9w8ODB7H2Xl4uudXsnL1y4MJv/Y489Zsvq9tN1UbduyNAFYrmIWPfeKKhbl+oieN07XBSy24fbRVLv3r3b6uciU53mLvLV5ePa2unn6hYVcLbsOmOnq6uDE6lu3tc94+zM2YCL+nY6uR3I6vRzkfxuZYOrm9N7+fLl2UfcWQB1u6i5tnZ1eOihh7Lvdu9wK1ief/55+62sXx87Pfdel19+efb+9u3bs/cXLVrUSaZ1slKtfqoAakBdUQBQp+YAqEs9AHVqG4B6UNK0fw5QA2pADajxqCs2gEeddgl41O3BOuwnADWgBtSAGlADajH0PWy8Di8/QA2oATWgBtSAGlAPj6tDzwlQA2pADagBNaAG1EPH6/AyBNQVLbds2ZKN+nYRrnUnJ7kmcpGpLoLX7fPshqnabmXoyhPld9G1hw7FmSe915w5c1pZposGd3NikbmLwB8ZGcm+u+0aUNem7r6LgI/CuEAs19Zur2UXOXz8+PFsnd2e63V7LbsoeFdWd9/tMe1WKbhvq04/F4Hf9nt0dd66dWtWV7dyIhI7Pdx89/333599x5lnnpm975YNumVydXa5d+/e7Dvanmng7NV9o3V9jfub6yNcYN/mzZs7ybROVqoVLQC1lQtQl9IA6tRMAHUzPQB1qROgHpRMEqAG1IC6ogAedTMAAepmOgFqQD04nssnATWgBtSAWgx9l0bA0HfaJbghboa+h4HgZnkAakANqAE1oK7YAKAG1M3wefJSAWpADagBNaAG1CKY7OSBt+2bAHVFsR07drSK+nZ7Nru9dONVLgrZ7Tvs9pJ2+z+74SgXSV1X1rr9gnOG5uYtXYS6C1Zzke7xTjeH7KJDXf3a7j/u6ubqEGVdtGhRq+/R6e2idOfNm5fN3+0P7+oQmaxbty6b1+joaPa+0899Ey6QyJ3QFi895ZRTsu++7LLLsvddVLvbX999cy6Suq6t3Ttc5P+9996brcP111/fqm779+/Ppnd7g0diV2933+3H7voUB/wHH3zQfg8uqr0uej2X2cqVKzvJtE5WqlXvCKitXIC6lAZQp2YCqFM9AHWpB6AelED+OUANqAF1RQG3rhdQA2o86tQG8KiHD2SXI6AG1IAaUDP0XbEBhr7TLoGh75MHZEDdQGvmqFORGPpm6Js56tIG8KjxqBtgZFyS4FHjUeNR41HjUeNR2yAzPOpxYW+rTLsA6s9LWiLpu5JmF7X/qKTVkr5X/PeHJH2lnzJ41HjUzFGnNoBHjUcNqPuRY/z/3gVQXyjpmKRYR1IFddz7RBsJb7zxxlbLs9xSq0EOunDDzG65lYu6dUub3LIZl0+dbu7gj7ZLydxyELfhfpTJlbftciunq2tTp0fdUjIHfdfWW7Zsyb5m9er4zdl7XXDBBdn77r11h504PdxhGq4OTj+389mGDRusqbmlOa5+ri3cj41Vq1Zl3+3suG4qyH3z7oAKZ6+PPvpotkxXXnll9r7L3y2JjExcmy5dujT7Dtembjmc08K9N17qDnNxy9iWLAnfrPe67rrrusC0nop1pVIzJMXCREA9fXrWgAF1KgugbqYHoC51AtSpzQDqNm7gS0vbZVC/W9Lzkr4u6YOSnusnFR51P4Ve/DsedakTHnVqM3jUqR541KUeeNTN+tdcqq6C+vWSnpEUQ9mbYsRU0nuMTNdIin+6+OKL57zlLW/pSeaG2hj6TqVi6DvVg6HvUg+GvlPbYOg71YOh73qIdxXU1VqfOCxuFcGjbvaLD48aj5o56vRbYY661IM56mb9aJtUXQV1eNBPFULcIGmupOX9hAHU/RRi6PtEhRj6Zug7FADUgLpZ7zlYqi6A+i5JCySdIek7kj5S/HeMYcfQ92FJ762A2yp17bXXZqO+3d61bmiz7qCLnTt3Zt/f9sxXF6Hpjuhzh3jURTm7qGIXPewOg5g2bVq2znWRqa6RXKS4a4sjR45ks3JRtC4fV4fbbrvN2tMZZ4RJ9l6u7dramWuHQ4cOZd978cUX27K6es+YEQNSvZcbqnRbrbofNO5QibruzOXlbMN5/24DE3eYigvIjLI6+zh+/Hirntn1He4wmn379mXzd1MNkdi1nXuH+xHiprrcXHSdFm6lxze+8Y1s/Vw/t3Hjxi4wrafOnaxUqy+jkhhQp8oB6lIPQJ3aBqBO9QDUpR6AelAC+ecANaC21gGoATUedWkDeNRpV4FHPXwguxwBNaAG1BUFGPpOzQFQA2qGvk8ekAF1A60Z+mboG1ADauaoUxsA1A3gMc5J8KjxqPGo8ahFMFlpBIAaUI8zd1tnD6grkm3atCkb9e2iRl2kYt1Wg22HEl1kr+tM3L7GzzwT+7/0XnPmzLFG44JC2u637XRyezm76PEo6Lx587Llff/735+9v379+ux9d+awiyh2dXb5xEvdMy46ue4YxVwlXOTryMhIts51S8leeOGF7DPOzpxdOj2WLVuWzb+urd2csFvH76LpXTSzW/HgItHrVnO4VRuurV3dnH27b+j552Pzxd5r5syZ9rtes2ZN9m+33357K4CsWLEim971G67dIhMXEe5s1ulx0003dZJpnaxUK2sD1IC6ogCgTs0BUJd6AOrUNgD1oKRp/xygBtSAGlALj7o0AjzqZiAB1M10GkYqQA2oATWgBtQVGwDUzdACqJvpNIxUgBpQA2pADagBtZijHgZSxycPQA2oATWgBtSAGlCPD2OHkiugrsi4a9eubNT35s2bs2K7/bPdjl6RiTuf1kWmrlu3Lvtu9+vX7fHr8nfRqvFSF7Hq5jNXrVqVLaurs4scriuT2w/blcmtAXXvdl/Vueeem/2T29s6Eru/uWhwt/f5gQMHsu9evXp19v7Bgwez9xcuXGg7DRdx/sQTT2SfcYFVrg5u+Vfdbl91Ueq5QrkVFW4o++EbowwAABPzSURBVLrrrsvWzZXJ7cQVmaxdu7ZVW7jv0dmGW0niyuQi4KOQGzduzJbV9VsuOt5F+Luy1n0rbfstp9Py5cs7ybROVmrQnzCAOlUOUJd6AOrUNgB1qgegLvUA1IMSyD8HqPGorXUAakCNR13aAB512lXgUQ8fyC5HQA2oAXWD7w2PGo8aUAPqBl3FuCQB1IAaUDf4tAA1oAbUgLpBVzEuSQA1oAbUDT4tQA2oATWgbtBVjEsSQF2R9cYbb8xGfbfdb7Zuq8G2Zzy7VncRlC6oxe2N66JP470rV67Mvt7l5aJu3X7OLnKzbk9gNy/m6lEX/drmi9q5c2c2udMoErv6OT3cfLCLgndR9vv27cuWtQ40LgCobWBQ25OWtm3bZpvB7VvvIsjdd+r2nnbfYlsbiwpMmzYtWw8XTe/e7ezYtYPLf/ny5VZX9465c+dmn9m+fXv2vquDi21x9yPzur/lXu5sY9++fZ1kWicr1aYDrqYF1KlygLrUA1CntuF+KALqVCdAXeoBqAclkwSo8ait9QBqQI1HXdpA3egTHnV/IANqQD24AoAaUDewHjxqPGpAndoAQ98NOo4hJcGjBtSAusHHBKgBNaAG1A26inFJAqgBNaBu8GkBakANqAF1g65iXJJ0AdRvkDQq6UxJP5b0WUm3SjpdUoTqzpB0WNK7JD1Xp+Lu3buzUd979+7NPub2wD1+/Lh9jYtYdXNcbg9ct1e122vZRfy64J+oQNtIahet7faqdtHPS5Yssfrdf//92b+55VOuDi4y30VYu+Cpur2q3dDg/Pnzs3Vwebk2clHis2bNyuZ/7bXXWl2vuOKK7N9c1Lzba97NaTu93T7wUZgHHnggW6b169dn77tvy9mAi5p3P8rcfvJRGPf9ur5j0aJFrerg9rh3KzDcioN4qbOnuh8iucK6Orjvum6vb/fduXMW3H7lq1at6gLTeuTuQqXOipUwkh6RdJqkOMHgUknvlnRU0i2S4st+raT8CReFLIA6tQ9AXeoBqFPbANSpHoC61ANQD9+p7gKoT1Tly5JuK/4tCMewAHn8PJ+JR50qgEed6oFHneqBR13qgUed2gYe9fCB7HLsGqhjmDt2e5gt6UlJr6lUPIa9w6u2Fx41HjWgBtQMfTcDEKBuptMwUnUJ1KdKisnkmyXtkvT9hqC+RlL80/XXXz/nHe94R4+uzFE367yZo051Yo661IM56mbeqJtnZ4461Y856mHg/+TncYqkeyV9VdIni9cfksTQdyEGwWSpURJMlupBMFmpBx51sw4cj7qZTsNI1QWPOupwZxE4trYiysclPVsJJoso8BsZ+k4VYI461YOh72ajJwSTpToRTFbqQTDZMNCc5tEFUL9d0tckPVosz4oafkjSw5K+KOmNxXz1OwuYWxVHRkayy7NcEIn7ON0SqXjxnj17su93B1q4JThr1qzJ5tO2TK7DjczbLtdwXqr7MeD2QXb3o0xu6dGyZcuyerjlK247Q7e8yB10UdfWztDckPi8efOyj+zaFTM5vZdra6dR3aEcru3q2iJXJrcsyEXNuyVSkbcrr9Pc6ee+X7cczi2JrFtK5pZqurZ2dunycbru378/axt1BwM5u2zbdu67dktH6/BVt5ws95z7ThctWtQFpvVUuZOVGvT3DKBOlQPUpR6AutlX1bazB9SproC6mZ0B6mY6dTIVoAbUeNSpDeBRl3rgUae2gUd98jCIR13RGlADakANqBn6Tm2g7ZGmDH0PH+CAGlBbq2Lom6FvPGo8akA9fPC2zRFQA2pAXVEAjxqPGo8aj7otSMc7PaCuKLxixYps1LdbsuM2+HAR2fGqHTt2tGpTt1bx2LFj2XxchKsLUnEHZkTm7h1ubsqlP3LkSLasTz/9dPb+ihUrrEZunnDlypXZZ9ww3JYtW7Lpt27d2qp9nG1EJu7d7seA089tuOMOLHDR0nUHiLggMGdP06dPz+rkbH/79u3Z9O4wlUjsyuRswOXl6u1WCjj96srqvi83KuXuO11dJL+zmbooahc17+zV5eXK2rbO0dbuO3LfilvrvnPnzk4yrZOVatXTAmorF6Dub0mAOtUIUKd6tIUWoE71A9Qv6gGoATWgriiAR52aAx51qQcedWobeNT9f8gPKwWgBtSAGlDbYWZADagZ+h4WbgfPB1ADakANqAF1xQaYo067BEA9OGCH9SSgBtSAGlADakBtgx8B9bBwO3g+gLqi3YYNG7JR305eF2xVF2Dk5nUWL16cfY2LlnV7CLvIzaVLl2bzv//++631nH56nGPSe7l9sl1G7ug+d9/tex75t913/fHHH88Wy3U+LvjHRd/XaXH8+PHsu12EsFuv6nRygVtuH2mXfxSy7V7fLsjHvePCCy/ManHw4EFrf04/t1JhwYI4LK/3cmV1L3bfr9t3P/Jx73Bt4ezJvcNFajv7c/1MlNXp6qLpTzklDifsva6//vrsfddvuFUKkcnIyEg2L7eO3+l6xx13dJJpnazUoL9bAHWqHKAu9QDUqW0A6mZ6AOpSJ0A9KJmI+k6UA9SAGo86tQHn0QBqQI1HPTh42z6JR11RDFADakANqBn6Tm2Aoe+2WB1+ekANqK1VMfTN0DcedWkDzFGnXQUe9fCB7HIE1IAaUFcUwKPGo8ajxqM+eQhu9iZAXdFp//792ahvFw3ZtlOPV7l9m130potOdpHALup2+fLlWYuo2//50KFD2WdmzpyZve86OFdWt2d4XXRy2y0F165dmy3rvn37mn0hRSq3K1XdCWOufu4Z5722DWRzNlYXYe2isp3ebnWBO9DClcnVLWR3gVhz5szJtt3hw4dbtamzS7cHeN1JYm3PZnajVW4VxsKFC7N1cytP6rRwqw7c9+vSu77JrbRwGkXFtm3blq2fW63i+ssbbrihk0zrZKVafa2A2soFqEtpAHVqJoA61QNQl3oA6kEJ5J8D1IAaUDf4rgA1oMajTm0Aj7pBxzGkJIAaUAPqBh8ToAbUgBpQN+gqxiUJoAbUgLrBpwWoATWgBtQNuopxSQKoATWgbvBpAWpADagBdYOuYlySdAHUb5A0KulMST+W9FlJt0r6qKTVkr5XKPchSV+pU3F0dDQb9e0iVs8777xsdnX7Px85ciT7jItwXb06qtB7ufkhF8Xtonfrop/dfuIuWMTtF+2inF3UrYsyDRVcRPjll1+e1cml37NnTzb9+vXrs/fdOdUrVqywJrVz587s31wkugOB08Pp6vaFdqsXopArV67MltXtre6itZ09uWCr/fv3W/2mTZuW/Zvbq9rp4SKpXTu407NcBHKdXbq+wEVY160iyInhvuujR49aXd135yL/255p4L65ukh015e6vubss8/O1m/jxo1dYFpP3bpQqbMkxb9HJJ0m6YCkSyW9S9IxSZ9o+hMHUKdKAepSD0Cd2gagTvVwcALUpU6AuimJetN1AdQn1urLkm6T9MuAupQGjzo1EzzqVA886lIPPOrUNvCoBwfssJ7sGqhnSIqdLGZL+oCkd0t6XtLXJX1Q0nN1wuFR41Ez9J3aAEPfpR4Mfae24YbvGfoeFp7LfLoE6lNj4y9JN0vaJen1kp6RFPPOm4rh8fdkJLxGUvzTVVddNeeiiy7qScIcdSoJc9SlHsxRp7bBHHWqB0PfDH0PA9tdAXWcbH6vpK9K+mRGmPC04+/hadsLjxqPGo8aj5qhb4a+hwHXYebRBVBHHe6UFGGO1Y2dI8DsqUKsGyTNlZTf8LpItGvXrmzUt4vEdPfror5dZO/8+fOz7eoCMNpGSbq9vt1oQRTG1c8FVt1+++3ZOkyfPj1732nh0kcmTg8XBe90ahsx7crkttKMsrroZBex6oaZndfu9u6eNWtWVu8673/BggXZZ1wkcNvoZDccWhfh74ZW3YjOjBnxe7z3ckGRriN1beq0iHxc/VwEvovKPnAgYmF7L9c/uBO93N7gkbN7944dO7Lvdt+Waztn9y6KP17qbNb1pc7+2Ot7mD8PhpvX2yV9TdKjxfKsyD2WYl0h6S3F0Hfs1v/eCrizJQDUqSyAutQDUDezDfdpA+pUGUCd6gGo66HYBY96aNgH1M06YzzqUic86mafH6AG1HjUzb6VXCpAXVEFUANqN1SJR93MNvCoUwUY+i71ANSAenAFALXVjqFvhr6Zoy5tgDnqtKtgjnoo2GmUCR41oAbUFQXwqFNzANSAmmCyRiwd10SAGlADakAtor77xx3gUeNRjyuNazIH1A1A7QJh2i47ile5X6duGYJbwtR2yYnLx5UnyuqWC82bNy9rUm7plDuswx0g4g4NiJe6JTju0Ae3JtadhuXm0dyhKXWdd9vlZ84GXN3c8qW2SwBDV/fMxo0bs239qU99KnvfDYe6/Ac5kcrZrLM/t4TJ7Ve+atWqbN02bNhgu1K3DNC1UdsDR1yUuNNvy5YttqxuUxq3VNN9c5dcckkrbo2OxtlJ+ct98872XT4sz2rVJJMzsQsmA9RpewLqZsOhgLrUCVCn3xCgTvUA1PXMxKPGo7YWgkddSoNHnZoJHnWqBx51f+cMj7q/Ri4FoAbUgLqiAEPfqTkw9F3qwdB3ahsMfQ8O3rZPAmpADagBNXPUFRtgjjrtEpijbovV4acH1IAaUANqQA2oRTDZ8AE7rBwBdQNQu0AYF+Fad2CB27Debbq/c+fObFu7qM5Nm+JEz97LlbXuAIy77747m9fNN8dJor3XvffGAWW919NPP5297wJI1q6tnq2SPuoOonDR1y7q27VR2yhTNzcZpd6/f3+23i6627XRkSNHsvm4wz1cHXbtitNf85d7xk0FuAhrl/7SSy/NvthtRxuJXZmOHTuWzcutInDp16xZk83H2UbdATaurK7vWLZsWfbdixcvzt7ftm1bq+/aaRGZOJt136Nra+dpu2h6F9cQZXL9n7NXFzOyffv2TjKtk5WyvVGfP7iob0CdCgeoSz0AdWobgDrVA1CXegDqQckkAeqKdoA6NSQ86v4fFqAG1HXeP6AG1P17kf4pADWgtlYCqPt/QIAaUAPq1AYY+u7fb7RNAagBNaCuKMAcdTPwMked6sQcdakHoG6L4f7pATWgBtSA2gZuEUxWGgfBZGlXQTBZf8AOKwWg9kpeI+mzwxJ6kuQzFescTTMV6z0V60xbT5KOiGKmCgBqbxFfl/S2KWYwU7HO0cRTsd5Tsc609RTr0LpSXUANqKsK0Hl35cvuXw/aur9GXUkxVdu6K+3H8qyalpyKxj0V64yX1ZnurFFFpqKNT8U6NzKGyZIIj5o56qoCzFtOli/3pZeTtn7pGk6WHKZqW0+W9ulbTkDdVyISoAAKoAAKoMDLpwCgfvm0580ogAIogAIo0FcBQN0r0a9JulXST0q6XdItfVWcnAk+L2mJpO9Kml1U4XRJcQrIDEmHJb1L0nOTs3rZUr9B0qikMyX9uFh+F23d9Xq/WtI+SdMkvVLSH0r6iKQ3SdpR1P8RSSsk/aBD7R1Vie845mj/srD3qVDn+HZfkPQjST8sVq903cY7ZrZpdQB1qkd81I9LWiTpLyT9qaQrJP15B63gQklxDFGAawzUmyUdLX6crJf0WknrOlT3syTFv4DSaZIOSIpjnd7d8XrHd/4zRXufIulPJL1f0gckxZFaAevfk/RfJX26Q+0dVYk6xjLLny1A/cUpUOcAddT5mUpbdv3b7pjZAuq6Bp0n6aOSfrVI9NvF//5uR60gPOc4m3IM1IckLZD0VAG0ByTN7Gjdo1pflnRb8W+q1PunC1BfK2l3MboQXteJtt+FZv85SXdKinNZA9i/Lul7Ha9ztFsO1FPt2+6C/f5NHfCo0+b8DUkx9L2quB1DgXMlXd+pVi8rcyKovy/pNZW6xrB3eNVdvKLuMRwcP1KenAL1jtGiGEH4BUmfkvRxSQ8V/x3tG9MCf1T50daFNo8h/viRHaMn/6IYOel6naPdvlVMWf21pM8UUzxT6dvugu0mdQDUaZO+s/Cmq6D+JUnv61zLv1ihqQrqUyXtLTytGPqdSp1Y/BC7R9LvSPr9E0D9FUnnd8TWI/5isaQ1xShRgPoqSfs7XOexpjtb0hFJr5N0X9F//Ycp8GO0I6bbWw1AnWrC0Hf3h75jjjaG+78q6ZNF80+1YcEIJPu/RfxBBNZ1ceg7POkYEYu6RTBdzFHHD5SY1upqnXOgiqm8iEVZPcWmtToFbUCdNmdExEYw2a8UUaIRTPabkg52qtX90HcMhz5bCSaLSNEbO1T3sPeYs4yAubWVenW93n9b0l8VIwc/JWmPpBFJV0q6uxJM9meStnWovceqEvEH4VGHl/0HHa9zBA2+ooj6jv8fHvXHij6ty992B822rBKg7m3eGC7bWizriCVMEYjSxeuu4hf2GZK+UyzX+ZKkiIp9YzFvG1MBAbWuXG+X9DVJjxbLs6JeH5L0cMfr/YvFD5SYp45OPNo4Ou83V5ZnfUPSb0k63pXGrtSjCuqu1znqFyMHcYXj8YWiD5vecRvvoNkC6k43KpVDARRAARTojgJ41N1pS2qCAiiAAijQQQUAdQcblSqhAAqgAAp0RwFA3Z22pCYogAIogAIdVABQd7BRqRIKoAAKoEB3FADU3WlLaoICKIACKNBBBQB1BxuVKqEACqAACnRHAUDdnbakJiiAAiiAAh1UAFB3sFGpEgqgAAqgQHcUANTdaUtqggIogAIo0EEFAHUHG5UqoQAKoAAKdEcBQN2dtqQmKIACKIACHVQAUHewUakSCqAACqBAdxQA1N1pS2qCAiiAAijQQQUAdQcblSqhAAqgAAp0R4H/D32to63MWCBOAAAAAElFTkSuQmCC” width=”490”>”

], “text/plain”: [

“<IPython.core.display.HTML object>”

]

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

}, {

“data”: {

“application/vnd.jupyter.widget-view+json”: {

“model_id”: “cb2c84c7925f4ca6aaf2676706b6ec24”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“interactive(children=(IntSlider(value=0, description=’Frame’, max=9999), Output()), _dom_classes=(‘widget-inte…”

]

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

}

], “source”: [

“%matplotlib notebookn”, “n”, “images = data.Acquisition.Sensor_Data.SensorData_1_1n”, “num_of_images = images.shape[0]-1n”, “n”, “fig = plt.figure(figsize=(0.681*10, 0.328*10))n”, “ax = fig.add_subplot(1,1,1)n”, “plt.title(‘Sensor Data 1 1’)n”, “plt.box(False)n”, “fig.show()n”, “#print(images.shape)n”, “def updateFrame(Frame):n”, ” ax.imshow(images[Frame,::], cmap=”gray”)n”, ” n”, “_ = interact(updateFrame, Frame=widgets.IntSlider(min=0,max=num_of_images,step=1,value=0))”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“#### EventStream<a id=’eventStream’></a>n”, “n”, “EventStreams can be a 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”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

“cell_type”: “code”, “execution_count”: 14, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

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

“——————————————————————————-n”, “Parent Group: <class ‘McsPyDataTools.McsPy.McsCMOSMEA.McsEventStream’ object at 0x246d9737ef0>n”, “——————————————————————————-n”, “n”, “n”, “| Mcs Type | HDF5 name | McsPy name |\n", "===============================================================================\n", "Groups:\n", " None\n", "-------------------------------------------------------------------------------\n", "Datasets:\n", "| EventData | EventData | EventData |\n", "| EventMeta | EventMeta | EventMeta |\n", "| Sites | StimulationSites | StimulationSites |n”, “n”

]

}

], “source”: [

“print(data.Acquisition.STG_Sideband_Events)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“We prepare the data:”

]

}, {

“cell_type”: “code”, “execution_count”: 16, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [], “source”: [

“# GET pandas DataFrame FROM MCSHDF5 FORMATn”, “eventData = data.Acquisition.STG_Sideband_Events.EventData.to_pdDataFrame().iloc[:50,:]n”, “eventData = eventData.sort_values(by=’EventID’)n”, “n”, “# EXTRACT EVENT POSITION IN TIMEn”, “eventPosition = eventData.pivot(columns=’EventID’, values=’TimeStamp’)n”, “eventPosition.fillna(method=’ffill’, axis=’index’, inplace=True)n”, “eventPosition.fillna(method=’bfill’, axis=’index’, inplace=True)n”, “n”, “# EXTRACT EVENT DURATIONn”, “eventDuration = eventData.pivot(columns=’EventID’, values=’Duration’)n”, “eventDuration.fillna(method=’ffill’, axis=’index’, inplace=True)n”, “eventDuration.fillna(method=’bfill’, axis=’index’, inplace=True)n”, “eventDuration[eventDuration == 0] += 0.2 # add minimum event durationn”, “n”, “# EXTRACT EVENT DESCRIPTIONn”, “eventMeta = data.Acquisition.STG_Sideband_Events.EventMeta.to_pdDataFrame().iloc[:50,:]n”, “eventMeta = eventMeta.sort_values(by=’EventID’)n”, “eventLabels = eventMeta.loc[eventMeta[‘EventID’].isin(eventData.EventID.unique())][‘Label’].tolist()”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“And visualize it”

]

}, {

“cell_type”: “code”, “execution_count”: 17, “metadata”: {

“pycharm”: {

“is_executing”: false

}

}, “outputs”: [

{

“data”: {

“application/javascript”: [

“/* Put everything inside the global mpl namespace /n”, “window.mpl = {};n”, “n”, “n”, “mpl.get_websocket_type = function() {n”, ” if (typeof(WebSocket) !== ‘undefined’) {n”, ” return WebSocket;n”, ” } else if (typeof(MozWebSocket) !== ‘undefined’) {n”, ” return MozWebSocket;n”, ” } else {n”, ” alert(‘Your browser does not have WebSocket support. ‘ +n”, ” ‘Please try Chrome, Safari or Firefox ≥ 6. ‘ +n”, ” ‘Firefox 4 and 5 are also supported but you ‘ +n”, ” ‘have to enable WebSockets in about:config.’);n”, ” };n”, “}n”, “n”, “mpl.figure = function(figure_id, websocket, ondownload, parent_element) {n”, ” this.id = figure_id;n”, “n”, ” this.ws = websocket;n”, “n”, ” this.supports_binary = (this.ws.binaryType != undefined);n”, “n”, ” if (!this.supports_binary) {n”, ” var warnings = document.getElementById(“mpl-warnings”);n”, ” if (warnings) {n”, ” warnings.style.display = ‘block’;n”, ” warnings.textContent = (n”, ” “This browser does not support binary websocket messages. ” +n”, ” “Performance may be slow.”);n”, ” }n”, ” }n”, “n”, ” this.imageObj = new Image();n”, “n”, ” this.context = undefined;n”, ” this.message = undefined;n”, ” this.canvas = undefined;n”, ” this.rubberband_canvas = undefined;n”, ” this.rubberband_context = undefined;n”, ” this.format_dropdown = undefined;n”, “n”, ” this.image_mode = ‘full’;n”, “n”, ” this.root = $(‘<div/>’);n”, ” this._root_extra_style(this.root)n”, ” this.root.attr(‘style’, ‘display: inline-block’);n”, “n”, ” $(parent_element).append(this.root);n”, “n”, ” this._init_header(this);n”, ” this._init_canvas(this);n”, ” this._init_toolbar(this);n”, “n”, ” var fig = this;n”, “n”, ” this.waiting = false;n”, “n”, ” this.ws.onopen = function () {n”, ” fig.send_message(“supports_binary”, {value: fig.supports_binary});n”, ” fig.send_message(“send_image_mode”, {});n”, ” if (mpl.ratio != 1) {n”, ” fig.send_message(“set_dpi_ratio”, {‘dpi_ratio’: mpl.ratio});n”, ” }n”, ” fig.send_message(“refresh”, {});n”, ” }n”, “n”, ” this.imageObj.onload = function() {n”, ” if (fig.image_mode == ‘full’) {n”, ” // Full images could contain transparency (where diff imagesn”, ” // almost always do), so we need to clear the canvas so thatn”, ” // there is no ghosting.n”, ” fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);n”, ” }n”, ” fig.context.drawImage(fig.imageObj, 0, 0);n”, ” };n”, “n”, ” this.imageObj.onunload = function() {n”, ” fig.ws.close();n”, ” }n”, “n”, ” this.ws.onmessage = this._make_on_message_function(this);n”, “n”, ” this.ondownload = ondownload;n”, “}n”, “n”, “mpl.figure.prototype._init_header = function() {n”, ” var titlebar = $(n”, ” ‘<div class=”ui-dialog-titlebar ui-widget-header ui-corner-all ‘ +n”, ” ‘ui-helper-clearfix”/>’);n”, ” var titletext = $(n”, ” ‘<div class=”ui-dialog-title” style=”width: 100%; ‘ +n”, ” ‘text-align: center; padding: 3px;”/>’);n”, ” titlebar.append(titletext)n”, ” this.root.append(titlebar);n”, ” this.header = titletext[0];n”, “}n”, “n”, “n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “n”, “mpl.figure.prototype._root_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “mpl.figure.prototype._init_canvas = function() {n”, ” var fig = this;n”, “n”, ” var canvas_div = $(‘<div/>’);n”, “n”, ” canvas_div.attr(‘style’, ‘position: relative; clear: both; outline: 0’);n”, “n”, ” function canvas_keyboard_event(event) {n”, ” return fig.key_event(event, event[‘data’]);n”, ” }n”, “n”, ” canvas_div.keydown(‘key_press’, canvas_keyboard_event);n”, ” canvas_div.keyup(‘key_release’, canvas_keyboard_event);n”, ” this.canvas_div = canvas_divn”, ” this._canvas_extra_style(canvas_div)n”, ” this.root.append(canvas_div);n”, “n”, ” var canvas = $(‘<canvas/>’);n”, ” canvas.addClass(‘mpl-canvas’);n”, ” canvas.attr(‘style’, “left: 0; top: 0; z-index: 0; outline: 0”)n”, “n”, ” this.canvas = canvas[0];n”, ” this.context = canvas[0].getContext(“2d”);n”, “n”, ” var backingStore = this.context.backingStorePixelRatio ||n”, “tthis.context.webkitBackingStorePixelRatio ||n”, “tthis.context.mozBackingStorePixelRatio ||n”, “tthis.context.msBackingStorePixelRatio ||n”, “tthis.context.oBackingStorePixelRatio ||n”, “tthis.context.backingStorePixelRatio || 1;n”, “n”, ” mpl.ratio = (window.devicePixelRatio || 1) / backingStore;n”, “n”, ” var rubberband = $(‘<canvas/>’);n”, ” rubberband.attr(‘style’, “position: absolute; left: 0; top: 0; z-index: 1;”)n”, “n”, ” var pass_mouse_events = true;n”, “n”, ” canvas_div.resizable({n”, ” start: function(event, ui) {n”, ” pass_mouse_events = false;n”, ” },n”, ” resize: function(event, ui) {n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” stop: function(event, ui) {n”, ” pass_mouse_events = true;n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” });n”, “n”, ” function mouse_event_fn(event) {n”, ” if (pass_mouse_events)n”, ” return fig.mouse_event(event, event[‘data’]);n”, ” }n”, “n”, ” rubberband.mousedown(‘button_press’, mouse_event_fn);n”, ” rubberband.mouseup(‘button_release’, mouse_event_fn);n”, ” // Throttle sequential mouse events to 1 every 20ms.n”, ” rubberband.mousemove(‘motion_notify’, mouse_event_fn);n”, “n”, ” rubberband.mouseenter(‘figure_enter’, mouse_event_fn);n”, ” rubberband.mouseleave(‘figure_leave’, mouse_event_fn);n”, “n”, ” canvas_div.on(“wheel”, function (event) {n”, ” event = event.originalEvent;n”, ” event[‘data’] = ‘scroll’n”, ” if (event.deltaY < 0) {n”, ” event.step = 1;n”, ” } else {n”, ” event.step = -1;n”, ” }n”, ” mouse_event_fn(event);n”, ” });n”, “n”, ” canvas_div.append(canvas);n”, ” canvas_div.append(rubberband);n”, “n”, ” this.rubberband = rubberband;n”, ” this.rubberband_canvas = rubberband[0];n”, ” this.rubberband_context = rubberband[0].getContext(“2d”);n”, ” this.rubberband_context.strokeStyle = “#000000”;n”, “n”, ” this._resize_canvas = function(width, height) {n”, ” // Keep the size of the canvas, canvas container, and rubber bandn”, ” // canvas in synch.n”, ” canvas_div.css(‘width’, width)n”, ” canvas_div.css(‘height’, height)n”, “n”, ” canvas.attr(‘width’, width * mpl.ratio);n”, ” canvas.attr(‘height’, height * mpl.ratio);n”, ” canvas.attr(‘style’, ‘width: ‘ + width + ‘px; height: ‘ + height + ‘px;’);n”, “n”, ” rubberband.attr(‘width’, width);n”, ” rubberband.attr(‘height’, height);n”, ” }n”, “n”, ” // Set the figure to an initial 600x600px, this will subsequently be updatedn”, ” // upon first draw.n”, ” this._resize_canvas(600, 600);n”, “n”, ” // Disable right mouse context menu.n”, ” $(this.rubberband_canvas).bind(“contextmenu”,function(e){n”, ” return false;n”, ” });n”, “n”, ” function set_focus () {n”, ” canvas.focus();n”, ” canvas_div.focus();n”, ” }n”, “n”, ” window.setTimeout(set_focus, 100);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items) {n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) {n”, ” // put a spacer in here.n”, ” continue;n”, ” }n”, ” var button = $(‘<button/>’);n”, ” button.addClass(‘ui-button ui-widget ui-state-default ui-corner-all ‘ +n”, ” ‘ui-button-icon-only’);n”, ” button.attr(‘role’, ‘button’);n”, ” button.attr(‘aria-disabled’, ‘false’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, “n”, ” var icon_img = $(‘<span/>’);n”, ” icon_img.addClass(‘ui-button-icon-primary ui-icon’);n”, ” icon_img.addClass(image);n”, ” icon_img.addClass(‘ui-corner-all’);n”, “n”, ” var tooltip_span = $(‘<span/>’);n”, ” tooltip_span.addClass(‘ui-button-text’);n”, ” tooltip_span.html(tooltip);n”, “n”, ” button.append(icon_img);n”, ” button.append(tooltip_span);n”, “n”, ” nav_element.append(button);n”, ” }n”, “n”, ” var fmt_picker_span = $(‘<span/>’);n”, “n”, ” var fmt_picker = $(‘<select/>’);n”, ” fmt_picker.addClass(‘mpl-toolbar-option ui-widget ui-widget-content’);n”, ” fmt_picker_span.append(fmt_picker);n”, ” nav_element.append(fmt_picker_span);n”, ” this.format_dropdown = fmt_picker[0];n”, “n”, ” for (var ind in mpl.extensions) {n”, ” var fmt = mpl.extensions[ind];n”, ” var option = $(n”, ” ‘<option/>’, {selected: fmt === mpl.default_extension}).html(fmt);n”, ” fmt_picker.append(option);n”, ” }n”, “n”, ” // Add hover states to the ui-buttonsn”, ” $( “.ui-button” ).hover(n”, ” function() { $(this).addClass(“ui-state-hover”);},n”, ” function() { $(this).removeClass(“ui-state-hover”);}n”, ” );n”, “n”, ” var status_bar = $(‘<span class=”mpl-message”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “}n”, “n”, “mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {n”, ” // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,n”, ” // which will in turn request a refresh of the image.n”, ” this.send_message(‘resize’, {‘width’: x_pixels, ‘height’: y_pixels});n”, “}n”, “n”, “mpl.figure.prototype.send_message = function(type, properties) {n”, ” properties[‘type’] = type;n”, ” properties[‘figure_id’] = this.id;n”, ” this.ws.send(JSON.stringify(properties));n”, “}n”, “n”, “mpl.figure.prototype.send_draw_message = function() {n”, ” if (!this.waiting) {n”, ” this.waiting = true;n”, ” this.ws.send(JSON.stringify({type: “draw”, figure_id: this.id}));n”, ” }n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” var format_dropdown = fig.format_dropdown;n”, ” var format = format_dropdown.options[format_dropdown.selectedIndex].value;n”, ” fig.ondownload(fig, format);n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_resize = function(fig, msg) {n”, ” var size = msg[‘size’];n”, ” if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {n”, ” fig._resize_canvas(size[0], size[1]);n”, ” fig.send_message(“refresh”, {});n”, ” };n”, “}n”, “n”, “mpl.figure.prototype.handle_rubberband = function(fig, msg) {n”, ” var x0 = msg[‘x0’] / mpl.ratio;n”, ” var y0 = (fig.canvas.height - msg[‘y0’]) / mpl.ratio;n”, ” var x1 = msg[‘x1’] / mpl.ratio;n”, ” var y1 = (fig.canvas.height - msg[‘y1’]) / mpl.ratio;n”, ” x0 = Math.floor(x0) + 0.5;n”, ” y0 = Math.floor(y0) + 0.5;n”, ” x1 = Math.floor(x1) + 0.5;n”, ” y1 = Math.floor(y1) + 0.5;n”, ” var min_x = Math.min(x0, x1);n”, ” var min_y = Math.min(y0, y1);n”, ” var width = Math.abs(x1 - x0);n”, ” var height = Math.abs(y1 - y0);n”, “n”, ” fig.rubberband_context.clearRect(n”, ” 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);n”, “n”, ” fig.rubberband_context.strokeRect(min_x, min_y, width, height);n”, “}n”, “n”, “mpl.figure.prototype.handle_figure_label = function(fig, msg) {n”, ” // Updates the figure title.n”, ” fig.header.textContent = msg[‘label’];n”, “}n”, “n”, “mpl.figure.prototype.handle_cursor = function(fig, msg) {n”, ” var cursor = msg[‘cursor’];n”, ” switch(cursor)n”, ” {n”, ” case 0:n”, ” cursor = ‘pointer’;n”, ” break;n”, ” case 1:n”, ” cursor = ‘default’;n”, ” break;n”, ” case 2:n”, ” cursor = ‘crosshair’;n”, ” break;n”, ” case 3:n”, ” cursor = ‘move’;n”, ” break;n”, ” }n”, ” fig.rubberband_canvas.style.cursor = cursor;n”, “}n”, “n”, “mpl.figure.prototype.handle_message = function(fig, msg) {n”, ” fig.message.textContent = msg[‘message’];n”, “}n”, “n”, “mpl.figure.prototype.handle_draw = function(fig, msg) {n”, ” // Request the server to send over a new figure.n”, ” fig.send_draw_message();n”, “}n”, “n”, “mpl.figure.prototype.handle_image_mode = function(fig, msg) {n”, ” fig.image_mode = msg[‘mode’];n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Called whenever the canvas gets updated.n”, ” this.send_message(“ack”, {});n”, “}n”, “n”, “// A function to construct a web socket function for onmessage handling.n”, “// Called in the figure constructor.n”, “mpl.figure.prototype._make_on_message_function = function(fig) {n”, ” return function socket_on_message(evt) {n”, ” if (evt.data instanceof Blob) {n”, ” / FIXME: We get “Resource interpreted as Image butn”, ” * transferred with MIME type text/plain:” errors onn”, ” * Chrome. But how to set the MIME type? It doesn’t seemn”, ” * to be part of the websocket stream /n”, ” evt.data.type = “image/png”;n”, “n”, ” / Free the memory for the previous frames /n”, ” if (fig.imageObj.src) {n”, ” (window.URL || window.webkitURL).revokeObjectURL(n”, ” fig.imageObj.src);n”, ” }n”, “n”, ” fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(n”, ” evt.data);n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, ” else if (typeof evt.data === ‘string’ && evt.data.slice(0, 21) == “data:image/png;base64”) {n”, ” fig.imageObj.src = evt.data;n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, “n”, ” var msg = JSON.parse(evt.data);n”, ” var msg_type = msg[‘type’];n”, “n”, ” // Call the “handle_{type}” callback, which takesn”, ” // the figure and JSON message as its only arguments.n”, ” try {n”, ” var callback = fig[“handle_” + msg_type];n”, ” } catch (e) {n”, ” console.log(“No handler for the ‘” + msg_type + “’ message type: “, msg);n”, ” return;n”, ” }n”, “n”, ” if (callback) {n”, ” try {n”, ” // console.log(“Handling ‘” + msg_type + “’ message: “, msg);n”, ” callback(fig, msg);n”, ” } catch (e) {n”, ” console.log(“Exception inside the ‘handler_” + msg_type + “’ callback:”, e, e.stack, msg);n”, ” }n”, ” }n”, ” };n”, “}n”, “n”, “// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvasn”, “mpl.findpos = function(e) {n”, ” //this section is from http://www.quirksmode.org/js/events_properties.htmln”, ” var targ;n”, ” if (!e)n”, ” e = window.event;n”, ” if (e.target)n”, ” targ = e.target;n”, ” else if (e.srcElement)n”, ” targ = e.srcElement;n”, ” if (targ.nodeType == 3) // defeat Safari bugn”, ” targ = targ.parentNode;n”, “n”, ” // jQuery normalizes the pageX and pageYn”, ” // pageX,Y are the mouse positions relative to the documentn”, ” // offset() returns the position of the element relative to the documentn”, ” var x = e.pageX - $(targ).offset().left;n”, ” var y = e.pageY - $(targ).offset().top;n”, “n”, ” return {“x”: x, “y”: y};n”, “};n”, “n”, “/n”, ” * return a copy of an object with only non-object keysn”, ” * we need this to avoid circular referencesn”, ” * http://stackoverflow.com/a/24161582/3208463n”, ” /n”, “function simpleKeys (original) {n”, ” return Object.keys(original).reduce(function (obj, key) {n”, ” if (typeof original[key] !== ‘object’)n”, ” obj[key] = original[key]n”, ” return obj;n”, ” }, {});n”, “}n”, “n”, “mpl.figure.prototype.mouse_event = function(event, name) {n”, ” var canvas_pos = mpl.findpos(event)n”, “n”, ” if (name === ‘button_press’)n”, ” {n”, ” this.canvas.focus();n”, ” this.canvas_div.focus();n”, ” }n”, “n”, ” var x = canvas_pos.x * mpl.ratio;n”, ” var y = canvas_pos.y * mpl.ratio;n”, “n”, ” this.send_message(name, {x: x, y: y, button: event.button,n”, ” step: event.step,n”, ” guiEvent: simpleKeys(event)});n”, “n”, ” / This prevents the web browser from automatically changing ton”, ” * the text insertion cursor when the button is pressed. We wantn”, ” * to control all of the cursor setting manually through then”, ” * ‘cursor’ event from matplotlib /n”, ” event.preventDefault();n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” // Handle any extra behaviour associated with a key eventn”, “}n”, “n”, “mpl.figure.prototype.key_event = function(event, name) {n”, “n”, ” // Prevent repeat eventsn”, ” if (name == ‘key_press’)n”, ” {n”, ” if (event.which === this._key)n”, ” return;n”, ” elsen”, ” this._key = event.which;n”, ” }n”, ” if (name == ‘key_release’)n”, ” this._key = null;n”, “n”, ” var value = ‘’;n”, ” if (event.ctrlKey && event.which != 17)n”, ” value += “ctrl+”;n”, ” if (event.altKey && event.which != 18)n”, ” value += “alt+”;n”, ” if (event.shiftKey && event.which != 16)n”, ” value += “shift+”;n”, “n”, ” value += ‘k’;n”, ” value += event.which.toString();n”, “n”, ” this._key_event_extra(event, name);n”, “n”, ” this.send_message(name, {key: value,n”, ” guiEvent: simpleKeys(event)});n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype.toolbar_button_onclick = function(name) {n”, ” if (name == ‘download’) {n”, ” this.handle_save(this, null);n”, ” } else {n”, ” this.send_message(“toolbar_button”, {name: name});n”, ” }n”, “};n”, “n”, “mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {n”, ” this.message.textContent = tooltip;n”, “};n”, “mpl.toolbar_items = [[“Home”, “Reset original view”, “fa fa-home icon-home”, “home”], [“Back”, “Back to previous view”, “fa fa-arrow-left icon-arrow-left”, “back”], [“Forward”, “Forward to next view”, “fa fa-arrow-right icon-arrow-right”, “forward”], [“”, “”, “”, “”], [“Pan”, “Pan axes with left mouse, zoom with right”, “fa fa-arrows icon-move”, “pan”], [“Zoom”, “Zoom to rectangle”, “fa fa-square-o icon-check-empty”, “zoom”], [“”, “”, “”, “”], [“Download”, “Download plot”, “fa fa-floppy-o icon-save”, “download”]];n”, “n”, “mpl.extensions = [“eps”, “jpeg”, “pdf”, “png”, “ps”, “raw”, “svg”, “tif”];n”, “n”, “mpl.default_extension = “png”;var comm_websocket_adapter = function(comm) {n”, ” // Create a “websocket”-like object which calls the given IPython commn”, ” // object with the appropriate methods. Currently this is a non binaryn”, ” // socket, so there is still some room for performance tuning.n”, ” var ws = {};n”, “n”, ” ws.close = function() {n”, ” comm.close()n”, ” };n”, ” ws.send = function(m) {n”, ” //console.log(‘sending’, m);n”, ” comm.send(m);n”, ” };n”, ” // Register the callback with on_msg.n”, ” comm.on_msg(function(msg) {n”, ” //console.log(‘receiving’, msg[‘content’][‘data’], msg);n”, ” // Pass the mpl event to the overridden (by mpl) onmessage function.n”, ” ws.onmessage(msg[‘content’][‘data’])n”, ” });n”, ” return ws;n”, “}n”, “n”, “mpl.mpl_figure_comm = function(comm, msg) {n”, ” // This is the function which gets called when the mpl processn”, ” // starts-up an IPython Comm through the “matplotlib” channel.n”, “n”, ” var id = msg.content.data.id;n”, ” // Get hold of the div created by the display call when the Commn”, ” // socket was opened in Python.n”, ” var element = $(“#” + id);n”, ” var ws_proxy = comm_websocket_adapter(comm)n”, “n”, ” function ondownload(figure, format) {n”, ” window.open(figure.imageObj.src);n”, ” }n”, “n”, ” var fig = new mpl.figure(id, ws_proxy,n”, ” ondownload,n”, ” element.get(0));n”, “n”, ” // Call onopen now - mpl needs it, as it is assuming we’ve passed it a realn”, ” // web socket which is closed, not our websocket->open comm proxy.n”, ” ws_proxy.onopen();n”, “n”, ” fig.parent_element = element.get(0);n”, ” fig.cell_info = mpl.find_output_cell(“<div id=’” + id + “’></div>”);n”, ” if (!fig.cell_info) {n”, ” console.error(“Failed to find cell for figure”, id, fig);n”, ” return;n”, ” }n”, “n”, ” var output_index = fig.cell_info[2]n”, ” var cell = fig.cell_info[0];n”, “n”, “};n”, “n”, “mpl.figure.prototype.handle_close = function(fig, msg) {n”, ” var width = fig.canvas.width/mpl.ration”, ” fig.root.unbind(‘remove’)n”, “n”, ” // Update the output cell to use the data from the current canvas.n”, ” fig.push_to_output();n”, ” var dataURL = fig.canvas.toDataURL();n”, ” // Re-enable the keyboard manager in IPython - without this line, in FF,n”, ” // the notebook keyboard shortcuts fail.n”, ” IPython.keyboard_manager.enable()n”, ” $(fig.parent_element).html(‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’);n”, ” fig.close_ws(fig, msg);n”, “}n”, “n”, “mpl.figure.prototype.close_ws = function(fig, msg){n”, ” fig.send_message(‘closing’, msg);n”, ” // fig.ws.close()n”, “}n”, “n”, “mpl.figure.prototype.push_to_output = function(remove_interactive) {n”, ” // Turn the data on the canvas into data in the output cell.n”, ” var width = this.canvas.width/mpl.ration”, ” var dataURL = this.canvas.toDataURL();n”, ” this.cell_info[1][‘text/html’] = ‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’;n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Tell IPython that the notebook contents must change.n”, ” IPython.notebook.set_dirty(true);n”, ” this.send_message(“ack”, {});n”, ” var fig = this;n”, ” // Wait a second, then push the new image to the DOM son”, ” // that it is saved nicely (might be nice to debounce this).n”, ” setTimeout(function () { fig.push_to_output() }, 1000);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items){n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) { continue; };n”, “n”, ” var button = $(‘<button class=”btn btn-default” href=”#” title=”’ + name + ‘”><i class=”fa ‘ + image + ‘ fa-lg”></i></button>’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, ” nav_element.append(button);n”, ” }n”, “n”, ” // Add the status bar.n”, ” var status_bar = $(‘<span class=”mpl-message” style=”text-align:right; float: right;”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “n”, ” // Add the close button to the window.n”, ” var buttongrp = $(‘<div class=”btn-group inline pull-right”></div>’);n”, ” var button = $(‘<button class=”btn btn-mini btn-primary” href=”#” title=”Stop Interaction”><i class=”fa fa-power-off icon-remove icon-large”></i></button>’);n”, ” button.click(function (evt) { fig.handle_close(fig, {}); } );n”, ” button.mouseover(‘Stop Interaction’, toolbar_mouse_event);n”, ” buttongrp.append(button);n”, ” var titlebar = this.root.find($(‘.ui-dialog-titlebar’));n”, ” titlebar.prepend(buttongrp);n”, “}n”, “n”, “mpl.figure.prototype._root_extra_style = function(el){n”, ” var fig = thisn”, ” el.on(“remove”, function(){n”, “tfig.close_ws(fig, {});n”, ” });n”, “}n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(el){n”, ” // this is important to make the div ‘focusablen”, ” el.attr(‘tabindex’, 0)n”, ” // reach out to IPython and tell the keyboard manager to turn it’s selfn”, ” // off when our div gets focusn”, “n”, ” // location in version 3n”, ” if (IPython.notebook.keyboard_manager) {n”, ” IPython.notebook.keyboard_manager.register_events(el);n”, ” }n”, ” else {n”, ” // location in version 2n”, ” IPython.keyboard_manager.register_events(el);n”, ” }n”, “n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” var manager = IPython.notebook.keyboard_manager;n”, ” if (!manager)n”, ” manager = IPython.keyboard_manager;n”, “n”, ” // Check for shift+entern”, ” if (event.shiftKey && event.which == 13) {n”, ” this.canvas_div.blur();n”, ” event.shiftKey = false;n”, ” // Send a “J” for go to next celln”, ” event.which = 74;n”, ” event.keyCode = 74;n”, ” manager.command_mode();n”, ” manager.handle_keydown(event);n”, ” }n”, “}n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” fig.ondownload(fig, null);n”, “}n”, “n”, “n”, “mpl.find_output_cell = function(html_output) {n”, ” // Return the cell and output element which can be found *uniquely in the notebook.n”, ” // Note - this is a bit hacky, but it is done because the “notebook_saving.Notebook”n”, ” // IPython event is triggered only after the cells have been serialised, which forn”, ” // our purposes (turning an active figure into a static one), is too late.n”, ” var cells = IPython.notebook.get_cells();n”, ” var ncells = cells.length;n”, ” for (var i=0; i<ncells; i++) {n”, ” var cell = cells[i];n”, ” if (cell.cell_type === ‘code’){n”, ” for (var j=0; j<cell.output_area.outputs.length; j++) {n”, ” var data = cell.output_area.outputs[j];n”, ” if (data.data) {n”, ” // IPython >= 3 moved mimebundle to data attribute of outputn”, ” data = data.data;n”, ” }n”, ” if (data[‘text/html’] == html_output) {n”, ” return [cell, data, j];n”, ” }n”, ” }n”, ” }n”, ” }n”, “}n”, “n”, “// Register the function which deals with the matplotlib target/channel.n”, “// The kernel may be null if the page has been refreshed.n”, “if (IPython.notebook.kernel != null) {n”, ” IPython.notebook.kernel.comm_manager.register_target(‘matplotlib’, mpl.mpl_figure_comm);n”, “}n”

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” width=”720”>”

], “text/plain”: [

“<IPython.core.display.HTML object>”

]

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

}

], “source”: [

“# VISUALIZE EVENTSn”, “fig = plt.figure(figsize=(10, 5))n”, “ax = fig.add_subplot(111)n”, “n”, “colors = [[0, 1, 0], [1, 0, 0]]n”, “n”, “ax.eventplot(eventPosition.values.transpose(), n”, ” linewidth=eventDuration.values.transpose(), n”, ” linelength = 0.62,n”, ” colors=colors)n”, “n”, “ax.set_title(‘Events in STG_Sideband_Events’)n”, “ax.set_xlabel(‘time [ms]’)n”, “ax.set_ylabel(‘Event’)n”, “ax.set_yticks(range(len(eventLabels)))n”, “ax.set_yticklabels(eventLabels)n”, “plt.box(False)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“## Processed Data Files (.cmtr)<a id=’processedData’></a>n”, “n”, “Processed data files with the file extension .cmtr are created by the CMOS-MEA-Tools software and contain the analysis results from different data sources. To access them, we need to create an instance of McsData in the same way as for the Raw Data files:”

]

}, {

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

“path2TestDataFile2 = os.path.join(path2TestData, “CMOS_MEA_Results.cmtr”)n”, “processed = McsCMOSMEA.McsData(path2TestDataFile2)”

]

}, {

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

“The easiest way to see the file contents is to print the object”

]

}, {

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

{

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

“<McsCMOSMEAData instance at 0x246d97f6ba8>n”, “n”, “This object represents the Mcs CMOS MEA file:n”, “Filename: result_file_with_network_explorer.cmtrn”, “n”, “Date Program Version n”, “——————- ————————– ———-n”, “16.12.2019 11:34:19 CMOS-MEA-Tools 2.4.0.0 n”, “n”, “n”, “Content:n”, “n”, “| Mcs Type | HDF5 name | McsPy name |\n", "===============================================================================\n", "Groups:\n", "| Acquisition | Acquisition | Acquisition |\n", "| SummaryTool | Activity Summary | Activity_Summary |\n", "| FilterTool | Filter Tool | Filter_Tool |\n", "| NetworkExplorerTool | Network Explorer | Network_Explorer |\n", "| SpikeExplorerTool | Spike Explorer | Spike_Explorer |\n", "| SpikeSorter | Spike Sorter | Spike_Sorter |n”, “——————————————————————————-n”, “Datasets:n”, ” Nonen”, “n”

]

}

], “source”: [

“print(processed)”

]

}, {

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

“The file contains 5 different data sources: Acquisition, Filter Tool, STA Explorer, Spike Explorer and Spike Sorter. Of these, Acquisition is a special case because it is actually a link to the Acquisition group in the Raw Data file 2017.12.14-18.38.15-GFP8ms_470nm_100pc_10rep_nofilter.cmcr that served as input to the analysis in CMOS-MEA-Tools. Still, you can access the contents of this group as usual:”

]

}, {

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

{

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

“——————————————————————————-n”, “Parent Group: <class ‘McsPyDataTools.McsPy.McsCMOSMEA.Acquisition’ object at 0x246d9971240>n”, “n”, “——————————————————————————-n”, “n”, “n”, “| Subtype | HDF5 name | McsPy name |\n", "===============================================================================\n", "ChannelStream\n", "| Digital | Digital Data | Digital_Data |\n", "-------------------------------------------------------------------------------\n", "EventStream\n", "| DigitalPort |EventTool @ Digital Data |EventTool_at_Digital_Data|n”, “——————————————————————————-n”, “SensorStreamn”, “| CMosSensor | Sensor Data | Sensor_Data |\n", "-------------------------------------------------------------------------------\n", "\n", "| Subtype | McsPy name | HDF5 name |\n", "=============================================================================================================\n", "SensorStream:\n", "| CMosSensor | Sensor_Data | Sensor Data |n”, “————————————————————————————————————-n”, “n”, “(205584, 65, 65)n”

]

}

], “source”: [

“print(processed.Acquisition)n”, “print(processed.Acquisition.SensorStreams)n”, “print(processed.Acquisition.SensorStreams[0].SensorData[0].shape)”

]

}, {

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

“For more details about the interaction with the Acquisition group, please refer to the <a href=’#RawData’>Raw Data</a> section above. Because it is a link to a different file, it is necessary that a file with the correct name is found in the same folder as the ProcessedData file.n”, “n”, “The other data sources correspond to different tools in the CMOS-MEA-Tools software. Please note that data sources are only present if they had been activated for the analysis in CMOS-MEA-Tools, so it is possible that other .cmtr files contain a different set of data sources.”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

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

“### Filter Tool<a id=’filterTool’></a>n”, “n”, “The Filter_Tool contains the settings for the filter pipeline applied to the raw data in CMOS-MEA-Tools:”

]

}, {

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

{

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

“——————————————————————————-n”, “Parent Group: <class ‘McsPyDataTools.McsPy.McsCMOSMEA.FilterTool’ object at 0x246d99718d0>n”, “——————————————————————————-n”, “n”, “n”, “| Mcs Type | HDF5 name | McsPy name |\n", "===============================================================================\n", "Groups:\n", "| PipeModel | Pipe | Pipe |\n", "-------------------------------------------------------------------------------\n", "Datasets:\n", "| SettingsFilterTool | SettingsFilterTool | SettingsFilterTool |n”, “n”, “Filter Order: 2n”, “Cutoff Frequency: 300.0 Hzn”

]

}

], “source”: [

“print(processed.Filter_Tool)n”, “print(“Filter Order: ” + str(processed.Filter_Tool.Pipe[‘1 High-Pass’][‘Order’][0]))n”, “print(“Cutoff Frequency: ” + str(processed.Filter_Tool.Pipe[‘1 High-Pass’][‘CutOffFrequency’][0]) + ” Hz”)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

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

“### Spike Explorer<a id=’spikeExplorer’></a>n”, “n”, “The Spike_Explorer holds settings for the channel-wise spike detector in CMOS-MEA-Tools, as well as its detected spikes:”

]

}, {

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

{

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

“——————————————————————————-n”, “Parent Group: <class ‘McsPyDataTools.McsPy.McsCMOSMEA.SpikeExplorer’ object at 0x246d97f6f60>n”, “——————————————————————————-n”, “n”, “n”, “| Mcs Type | HDF5 name | McsPy name |\n", "===============================================================================\n", "Groups:\n", " None\n", "-------------------------------------------------------------------------------\n", "Datasets:\n", "| SettingsMapCreator | SettingsMapCreator | SettingsMapCreator |\n", "| SettingsSpikeDetector | SettingsSpikeDetector | SettingsSpikeDetector |\n", "| SettingsSpikeExplorer | SettingsSpikeExplorer | SettingsSpikeExplorer |\n", "|SettingsSpikePeakActivity|SettingsSpikePeakActivity|SettingsSpikePeakActivity|n”, “| SpikeData | SpikeData | SpikeData |\n", "| SpikeMeta | SpikeMeta | SpikeMeta |n”, “n”

]

}

], “source”: [

“print(processed.Spike_Explorer)”

]

}, {

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

“The detection threshold and other details for the spike detector can be found in the SettingsSpikeDetector data set:”

]

}, {

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

{

“data”: {

“text/plain”: [

“5.0”

]

}, “execution_count”: 23, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“processed.Spike_Explorer.SettingsSpikeDetector[‘Threshold’][0]”

]

}, {

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

“The detected spikes are contained in the SpikeData dataset. It stores for each spike the SensorID of the detection location, the TimeStamp as the detection time in µs and an optional signal cutout around the spike timestamp:”

]

}, {

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

{

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

“Compound McsDataset SpikeDatan”, “n”, “location in hdf5 file: /Spike Explorer/SpikeDatan”, “shape: /Spike Explorer/SpikeData(18486,)n”, “dtype: /Spike Explorer/SpikeData[(‘SensorID’, ‘<i4’), (‘TimeStamp’, ‘<i8’), (‘1’, ‘<f8’), (‘2’, ‘<f8’), (‘3’, ‘<f8’), (‘4’, ‘<f8’), (‘5’, ‘<f8’), (‘6’, ‘<f8’), (‘7’, ‘<f8’), (‘8’, ‘<f8’), (‘9’, ‘<f8’), (‘10’, ‘<f8’), (‘11’, ‘<f8’), (‘12’, ‘<f8’), (‘13’, ‘<f8’), (‘14’, ‘<f8’), (‘15’, ‘<f8’), (‘16’, ‘<f8’), (‘17’, ‘<f8’), (‘18’, ‘<f8’), (‘19’, ‘<f8’), (‘20’, ‘<f8’), (‘21’, ‘<f8’), (‘22’, ‘<f8’), (‘23’, ‘<f8’), (‘24’, ‘<f8’), (‘25’, ‘<f8’), (‘26’, ‘<f8’), (‘27’, ‘<f8’), (‘28’, ‘<f8’), (‘29’, ‘<f8’), (‘30’, ‘<f8’), (‘31’, ‘<f8’), (‘32’, ‘<f8’), (‘33’, ‘<f8’), (‘34’, ‘<f8’), (‘35’, ‘<f8’), (‘36’, ‘<f8’), (‘37’, ‘<f8’), (‘38’, ‘<f8’), (‘39’, ‘<f8’), (‘40’, ‘<f8’), (‘41’, ‘<f8’), (‘42’, ‘<f8’), (‘43’, ‘<f8’), (‘44’, ‘<f8’), (‘45’, ‘<f8’), (‘46’, ‘<f8’), (‘47’, ‘<f8’), (‘48’, ‘<f8’), (‘49’, ‘<f8’), (‘50’, ‘<f8’), (‘51’, ‘<f8’), (‘52’, ‘<f8’), (‘53’, ‘<f8’), (‘54’, ‘<f8’), (‘55’, ‘<f8’), (‘56’, ‘<f8’), (‘57’, ‘<f8’), (‘58’, ‘<f8’), (‘59’, ‘<f8’), (‘60’, ‘<f8’)]n”, “n”

]

}

], “source”: [

“print(processed.Spike_Explorer.SpikeData)”

]

}, {

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

“This allows us to do different visualizations of the spike data:”

]

}, {

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

“#### Raster Plotn”, “n”, “We can generate a raster plot for one or more sensors:”

]

}, {

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

{

“data”: {

“application/javascript”: [

“/* Put everything inside the global mpl namespace /n”, “window.mpl = {};n”, “n”, “n”, “mpl.get_websocket_type = function() {n”, ” if (typeof(WebSocket) !== ‘undefined’) {n”, ” return WebSocket;n”, ” } else if (typeof(MozWebSocket) !== ‘undefined’) {n”, ” return MozWebSocket;n”, ” } else {n”, ” alert(‘Your browser does not have WebSocket support. ‘ +n”, ” ‘Please try Chrome, Safari or Firefox ≥ 6. ‘ +n”, ” ‘Firefox 4 and 5 are also supported but you ‘ +n”, ” ‘have to enable WebSockets in about:config.’);n”, ” };n”, “}n”, “n”, “mpl.figure = function(figure_id, websocket, ondownload, parent_element) {n”, ” this.id = figure_id;n”, “n”, ” this.ws = websocket;n”, “n”, ” this.supports_binary = (this.ws.binaryType != undefined);n”, “n”, ” if (!this.supports_binary) {n”, ” var warnings = document.getElementById(“mpl-warnings”);n”, ” if (warnings) {n”, ” warnings.style.display = ‘block’;n”, ” warnings.textContent = (n”, ” “This browser does not support binary websocket messages. ” +n”, ” “Performance may be slow.”);n”, ” }n”, ” }n”, “n”, ” this.imageObj = new Image();n”, “n”, ” this.context = undefined;n”, ” this.message = undefined;n”, ” this.canvas = undefined;n”, ” this.rubberband_canvas = undefined;n”, ” this.rubberband_context = undefined;n”, ” this.format_dropdown = undefined;n”, “n”, ” this.image_mode = ‘full’;n”, “n”, ” this.root = $(‘<div/>’);n”, ” this._root_extra_style(this.root)n”, ” this.root.attr(‘style’, ‘display: inline-block’);n”, “n”, ” $(parent_element).append(this.root);n”, “n”, ” this._init_header(this);n”, ” this._init_canvas(this);n”, ” this._init_toolbar(this);n”, “n”, ” var fig = this;n”, “n”, ” this.waiting = false;n”, “n”, ” this.ws.onopen = function () {n”, ” fig.send_message(“supports_binary”, {value: fig.supports_binary});n”, ” fig.send_message(“send_image_mode”, {});n”, ” if (mpl.ratio != 1) {n”, ” fig.send_message(“set_dpi_ratio”, {‘dpi_ratio’: mpl.ratio});n”, ” }n”, ” fig.send_message(“refresh”, {});n”, ” }n”, “n”, ” this.imageObj.onload = function() {n”, ” if (fig.image_mode == ‘full’) {n”, ” // Full images could contain transparency (where diff imagesn”, ” // almost always do), so we need to clear the canvas so thatn”, ” // there is no ghosting.n”, ” fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);n”, ” }n”, ” fig.context.drawImage(fig.imageObj, 0, 0);n”, ” };n”, “n”, ” this.imageObj.onunload = function() {n”, ” fig.ws.close();n”, ” }n”, “n”, ” this.ws.onmessage = this._make_on_message_function(this);n”, “n”, ” this.ondownload = ondownload;n”, “}n”, “n”, “mpl.figure.prototype._init_header = function() {n”, ” var titlebar = $(n”, ” ‘<div class=”ui-dialog-titlebar ui-widget-header ui-corner-all ‘ +n”, ” ‘ui-helper-clearfix”/>’);n”, ” var titletext = $(n”, ” ‘<div class=”ui-dialog-title” style=”width: 100%; 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z-index: 0; outline: 0”)n”, “n”, ” this.canvas = canvas[0];n”, ” this.context = canvas[0].getContext(“2d”);n”, “n”, ” var backingStore = this.context.backingStorePixelRatio ||n”, “tthis.context.webkitBackingStorePixelRatio ||n”, “tthis.context.mozBackingStorePixelRatio ||n”, “tthis.context.msBackingStorePixelRatio ||n”, “tthis.context.oBackingStorePixelRatio ||n”, “tthis.context.backingStorePixelRatio || 1;n”, “n”, ” mpl.ratio = (window.devicePixelRatio || 1) / backingStore;n”, “n”, ” var rubberband = $(‘<canvas/>’);n”, ” rubberband.attr(‘style’, “position: absolute; left: 0; top: 0; z-index: 1;”)n”, “n”, ” var pass_mouse_events = true;n”, “n”, ” canvas_div.resizable({n”, ” start: function(event, ui) {n”, ” pass_mouse_events = false;n”, ” },n”, ” resize: function(event, ui) {n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” stop: function(event, ui) {n”, ” pass_mouse_events = true;n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” });n”, “n”, ” function mouse_event_fn(event) {n”, ” if (pass_mouse_events)n”, ” return fig.mouse_event(event, event[‘data’]);n”, ” }n”, “n”, ” rubberband.mousedown(‘button_press’, mouse_event_fn);n”, ” rubberband.mouseup(‘button_release’, mouse_event_fn);n”, ” // Throttle sequential mouse events to 1 every 20ms.n”, ” rubberband.mousemove(‘motion_notify’, mouse_event_fn);n”, “n”, ” rubberband.mouseenter(‘figure_enter’, mouse_event_fn);n”, ” rubberband.mouseleave(‘figure_leave’, mouse_event_fn);n”, “n”, ” canvas_div.on(“wheel”, function (event) {n”, ” event = event.originalEvent;n”, ” event[‘data’] = ‘scroll’n”, ” if (event.deltaY < 0) {n”, ” event.step = 1;n”, ” } else {n”, ” event.step = -1;n”, ” }n”, ” mouse_event_fn(event);n”, ” });n”, “n”, ” canvas_div.append(canvas);n”, ” canvas_div.append(rubberband);n”, “n”, ” this.rubberband = rubberband;n”, ” this.rubberband_canvas = rubberband[0];n”, ” this.rubberband_context = rubberband[0].getContext(“2d”);n”, ” this.rubberband_context.strokeStyle = “#000000”;n”, “n”, ” this._resize_canvas = function(width, height) {n”, ” // Keep the size of the canvas, canvas container, and rubber bandn”, ” // canvas in synch.n”, ” canvas_div.css(‘width’, width)n”, ” canvas_div.css(‘height’, height)n”, “n”, ” canvas.attr(‘width’, width * mpl.ratio);n”, ” canvas.attr(‘height’, height * mpl.ratio);n”, ” canvas.attr(‘style’, ‘width: ‘ + width + ‘px; height: ‘ + height + ‘px;’);n”, “n”, ” rubberband.attr(‘width’, width);n”, ” rubberband.attr(‘height’, height);n”, ” }n”, “n”, ” // Set the figure to an initial 600x600px, this will subsequently be updatedn”, ” // upon first draw.n”, ” this._resize_canvas(600, 600);n”, “n”, ” // Disable right mouse context menu.n”, ” $(this.rubberband_canvas).bind(“contextmenu”,function(e){n”, ” return false;n”, ” });n”, “n”, ” function set_focus () {n”, ” canvas.focus();n”, ” canvas_div.focus();n”, ” }n”, “n”, ” window.setTimeout(set_focus, 100);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items) {n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) {n”, ” // put a spacer in here.n”, ” continue;n”, ” }n”, ” var button = $(‘<button/>’);n”, ” button.addClass(‘ui-button ui-widget ui-state-default ui-corner-all ‘ +n”, ” ‘ui-button-icon-only’);n”, ” button.attr(‘role’, ‘button’);n”, ” button.attr(‘aria-disabled’, ‘false’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, “n”, ” var icon_img = $(‘<span/>’);n”, ” icon_img.addClass(‘ui-button-icon-primary ui-icon’);n”, ” icon_img.addClass(image);n”, ” icon_img.addClass(‘ui-corner-all’);n”, “n”, ” var tooltip_span = $(‘<span/>’);n”, ” tooltip_span.addClass(‘ui-button-text’);n”, ” tooltip_span.html(tooltip);n”, “n”, ” button.append(icon_img);n”, ” button.append(tooltip_span);n”, “n”, ” nav_element.append(button);n”, ” }n”, “n”, ” var fmt_picker_span = $(‘<span/>’);n”, “n”, ” var fmt_picker = $(‘<select/>’);n”, ” fmt_picker.addClass(‘mpl-toolbar-option ui-widget ui-widget-content’);n”, ” fmt_picker_span.append(fmt_picker);n”, ” nav_element.append(fmt_picker_span);n”, ” this.format_dropdown = fmt_picker[0];n”, “n”, ” for (var ind in mpl.extensions) {n”, ” var fmt = mpl.extensions[ind];n”, ” var option = $(n”, ” ‘<option/>’, {selected: fmt === mpl.default_extension}).html(fmt);n”, ” fmt_picker.append(option);n”, ” }n”, “n”, ” // Add hover states to the ui-buttonsn”, ” $( “.ui-button” ).hover(n”, ” function() { $(this).addClass(“ui-state-hover”);},n”, ” function() { $(this).removeClass(“ui-state-hover”);}n”, ” );n”, “n”, ” var status_bar = $(‘<span class=”mpl-message”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “}n”, “n”, “mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {n”, ” // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,n”, ” // which will in turn request a refresh of the image.n”, ” this.send_message(‘resize’, {‘width’: x_pixels, ‘height’: y_pixels});n”, “}n”, “n”, “mpl.figure.prototype.send_message = function(type, properties) {n”, ” properties[‘type’] = type;n”, ” properties[‘figure_id’] = this.id;n”, ” this.ws.send(JSON.stringify(properties));n”, “}n”, “n”, “mpl.figure.prototype.send_draw_message = function() {n”, ” if (!this.waiting) {n”, ” this.waiting = true;n”, ” this.ws.send(JSON.stringify({type: “draw”, figure_id: this.id}));n”, ” }n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” var format_dropdown = fig.format_dropdown;n”, ” var format = format_dropdown.options[format_dropdown.selectedIndex].value;n”, ” fig.ondownload(fig, format);n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_resize = function(fig, msg) {n”, ” var size = msg[‘size’];n”, ” if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {n”, ” fig._resize_canvas(size[0], size[1]);n”, ” fig.send_message(“refresh”, {});n”, ” };n”, “}n”, “n”, “mpl.figure.prototype.handle_rubberband = function(fig, msg) {n”, ” var x0 = msg[‘x0’] / mpl.ratio;n”, ” var y0 = (fig.canvas.height - msg[‘y0’]) / mpl.ratio;n”, ” var x1 = msg[‘x1’] / mpl.ratio;n”, ” var y1 = (fig.canvas.height - msg[‘y1’]) / mpl.ratio;n”, ” x0 = Math.floor(x0) + 0.5;n”, ” y0 = Math.floor(y0) + 0.5;n”, ” x1 = Math.floor(x1) + 0.5;n”, ” y1 = Math.floor(y1) + 0.5;n”, ” var min_x = Math.min(x0, x1);n”, ” var min_y = Math.min(y0, y1);n”, ” var width = Math.abs(x1 - x0);n”, ” var height = Math.abs(y1 - y0);n”, “n”, ” fig.rubberband_context.clearRect(n”, ” 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);n”, “n”, ” fig.rubberband_context.strokeRect(min_x, min_y, width, height);n”, “}n”, “n”, “mpl.figure.prototype.handle_figure_label = function(fig, msg) {n”, ” // Updates the figure title.n”, ” fig.header.textContent = msg[‘label’];n”, “}n”, “n”, “mpl.figure.prototype.handle_cursor = function(fig, msg) {n”, ” var cursor = msg[‘cursor’];n”, ” switch(cursor)n”, ” {n”, ” case 0:n”, ” cursor = ‘pointer’;n”, ” break;n”, ” case 1:n”, ” cursor = ‘default’;n”, ” break;n”, ” case 2:n”, ” cursor = ‘crosshair’;n”, ” break;n”, ” case 3:n”, ” cursor = ‘move’;n”, ” break;n”, ” }n”, ” fig.rubberband_canvas.style.cursor = cursor;n”, “}n”, “n”, “mpl.figure.prototype.handle_message = function(fig, msg) {n”, ” fig.message.textContent = msg[‘message’];n”, “}n”, “n”, “mpl.figure.prototype.handle_draw = function(fig, msg) {n”, ” // Request the server to send over a new figure.n”, ” fig.send_draw_message();n”, “}n”, “n”, “mpl.figure.prototype.handle_image_mode = function(fig, msg) {n”, ” fig.image_mode = msg[‘mode’];n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Called whenever the canvas gets updated.n”, ” this.send_message(“ack”, {});n”, “}n”, “n”, “// A function to construct a web socket function for onmessage handling.n”, “// Called in the figure constructor.n”, “mpl.figure.prototype._make_on_message_function = function(fig) {n”, ” return function socket_on_message(evt) {n”, ” if (evt.data instanceof Blob) {n”, ” / FIXME: We get “Resource interpreted as Image butn”, ” * transferred with MIME type text/plain:” errors onn”, ” * Chrome. But how to set the MIME type? It doesn’t seemn”, ” * to be part of the websocket stream /n”, ” evt.data.type = “image/png”;n”, “n”, ” / Free the memory for the previous frames /n”, ” if (fig.imageObj.src) {n”, ” (window.URL || window.webkitURL).revokeObjectURL(n”, ” fig.imageObj.src);n”, ” }n”, “n”, ” fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(n”, ” evt.data);n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, ” else if (typeof evt.data === ‘string’ && evt.data.slice(0, 21) == “data:image/png;base64”) {n”, ” fig.imageObj.src = evt.data;n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, “n”, ” var msg = JSON.parse(evt.data);n”, ” var msg_type = msg[‘type’];n”, “n”, ” // Call the “handle_{type}” callback, which takesn”, ” // the figure and JSON message as its only arguments.n”, ” try {n”, ” var callback = fig[“handle_” + msg_type];n”, ” } catch (e) {n”, ” console.log(“No handler for the ‘” + msg_type + “’ message type: “, msg);n”, ” return;n”, ” }n”, “n”, ” if (callback) {n”, ” try {n”, ” // console.log(“Handling ‘” + msg_type + “’ message: “, msg);n”, ” callback(fig, msg);n”, ” } catch (e) {n”, ” console.log(“Exception inside the ‘handler_” + msg_type + “’ callback:”, e, e.stack, msg);n”, ” }n”, ” }n”, ” };n”, “}n”, “n”, “// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvasn”, “mpl.findpos = function(e) {n”, ” //this section is from http://www.quirksmode.org/js/events_properties.htmln”, ” var targ;n”, ” if (!e)n”, ” e = window.event;n”, ” if (e.target)n”, ” targ = e.target;n”, ” else if (e.srcElement)n”, ” targ = e.srcElement;n”, ” if (targ.nodeType == 3) // defeat Safari bugn”, ” targ = targ.parentNode;n”, “n”, ” // jQuery normalizes the pageX and pageYn”, ” // pageX,Y are the mouse positions relative to the documentn”, ” // offset() returns the position of the element relative to the documentn”, ” var x = e.pageX - $(targ).offset().left;n”, ” var y = e.pageY - $(targ).offset().top;n”, “n”, ” return {“x”: x, “y”: y};n”, “};n”, “n”, “/n”, ” * return a copy of an object with only non-object keysn”, ” * we need this to avoid circular referencesn”, ” * http://stackoverflow.com/a/24161582/3208463n”, ” /n”, “function simpleKeys (original) {n”, ” return Object.keys(original).reduce(function (obj, key) {n”, ” if (typeof original[key] !== ‘object’)n”, ” obj[key] = original[key]n”, ” return obj;n”, ” }, {});n”, “}n”, “n”, “mpl.figure.prototype.mouse_event = function(event, name) {n”, ” var canvas_pos = mpl.findpos(event)n”, “n”, ” if (name === ‘button_press’)n”, ” {n”, ” this.canvas.focus();n”, ” this.canvas_div.focus();n”, ” }n”, “n”, ” var x = canvas_pos.x * mpl.ratio;n”, ” var y = canvas_pos.y * mpl.ratio;n”, “n”, ” this.send_message(name, {x: x, y: y, button: event.button,n”, ” step: event.step,n”, ” guiEvent: simpleKeys(event)});n”, “n”, ” / This prevents the web browser from automatically changing ton”, ” * the text insertion cursor when the button is pressed. We wantn”, ” * to control all of the cursor setting manually through then”, ” * ‘cursor’ event from matplotlib /n”, ” event.preventDefault();n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” // Handle any extra behaviour associated with a key eventn”, “}n”, “n”, “mpl.figure.prototype.key_event = function(event, name) {n”, “n”, ” // Prevent repeat eventsn”, ” if (name == ‘key_press’)n”, ” {n”, ” if (event.which === this._key)n”, ” return;n”, ” elsen”, ” this._key = event.which;n”, ” }n”, ” if (name == ‘key_release’)n”, ” this._key = null;n”, “n”, ” var value = ‘’;n”, ” if (event.ctrlKey && event.which != 17)n”, ” value += “ctrl+”;n”, ” if (event.altKey && event.which != 18)n”, ” value += “alt+”;n”, ” if (event.shiftKey && event.which != 16)n”, ” value += “shift+”;n”, “n”, ” value += ‘k’;n”, ” value += event.which.toString();n”, “n”, ” this._key_event_extra(event, name);n”, “n”, ” this.send_message(name, {key: value,n”, ” guiEvent: simpleKeys(event)});n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype.toolbar_button_onclick = function(name) {n”, ” if (name == ‘download’) {n”, ” this.handle_save(this, null);n”, ” } else {n”, ” this.send_message(“toolbar_button”, {name: name});n”, ” }n”, “};n”, “n”, “mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {n”, ” this.message.textContent = tooltip;n”, “};n”, “mpl.toolbar_items = [[“Home”, “Reset original view”, “fa fa-home icon-home”, “home”], [“Back”, “Back to previous view”, “fa fa-arrow-left icon-arrow-left”, “back”], [“Forward”, “Forward to next view”, “fa fa-arrow-right icon-arrow-right”, “forward”], [“”, “”, “”, “”], [“Pan”, “Pan axes with left mouse, zoom with right”, “fa fa-arrows icon-move”, “pan”], [“Zoom”, “Zoom to rectangle”, “fa fa-square-o icon-check-empty”, “zoom”], [“”, “”, “”, “”], [“Download”, “Download plot”, “fa fa-floppy-o icon-save”, “download”]];n”, “n”, “mpl.extensions = [“eps”, “jpeg”, “pdf”, “png”, “ps”, “raw”, “svg”, “tif”];n”, “n”, “mpl.default_extension = “png”;var comm_websocket_adapter = function(comm) {n”, ” // Create a “websocket”-like object which calls the given IPython commn”, ” // object with the appropriate methods. Currently this is a non binaryn”, ” // socket, so there is still some room for performance tuning.n”, ” var ws = {};n”, “n”, ” ws.close = function() {n”, ” comm.close()n”, ” };n”, ” ws.send = function(m) {n”, ” //console.log(‘sending’, m);n”, ” comm.send(m);n”, ” };n”, ” // Register the callback with on_msg.n”, ” comm.on_msg(function(msg) {n”, ” //console.log(‘receiving’, msg[‘content’][‘data’], msg);n”, ” // Pass the mpl event to the overridden (by mpl) onmessage function.n”, ” ws.onmessage(msg[‘content’][‘data’])n”, ” });n”, ” return ws;n”, “}n”, “n”, “mpl.mpl_figure_comm = function(comm, msg) {n”, ” // This is the function which gets called when the mpl processn”, ” // starts-up an IPython Comm through the “matplotlib” channel.n”, “n”, ” var id = msg.content.data.id;n”, ” // Get hold of the div created by the display call when the Commn”, ” // socket was opened in Python.n”, ” var element = $(“#” + id);n”, ” var ws_proxy = comm_websocket_adapter(comm)n”, “n”, ” function ondownload(figure, format) {n”, ” window.open(figure.imageObj.src);n”, ” }n”, “n”, ” var fig = new mpl.figure(id, ws_proxy,n”, ” ondownload,n”, ” element.get(0));n”, “n”, ” // Call onopen now - mpl needs it, as it is assuming we’ve passed it a realn”, ” // web socket which is closed, not our websocket->open comm proxy.n”, ” ws_proxy.onopen();n”, “n”, ” fig.parent_element = element.get(0);n”, ” fig.cell_info = mpl.find_output_cell(“<div id=’” + id + “’></div>”);n”, ” if (!fig.cell_info) {n”, ” console.error(“Failed to find cell for figure”, id, fig);n”, ” return;n”, ” }n”, “n”, ” var output_index = fig.cell_info[2]n”, ” var cell = fig.cell_info[0];n”, “n”, “};n”, “n”, “mpl.figure.prototype.handle_close = function(fig, msg) {n”, ” var width = fig.canvas.width/mpl.ration”, ” fig.root.unbind(‘remove’)n”, “n”, ” // Update the output cell to use the data from the current canvas.n”, ” fig.push_to_output();n”, ” var dataURL = fig.canvas.toDataURL();n”, ” // Re-enable the keyboard manager in IPython - without this line, in FF,n”, ” // the notebook keyboard shortcuts fail.n”, ” IPython.keyboard_manager.enable()n”, ” $(fig.parent_element).html(‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’);n”, ” fig.close_ws(fig, msg);n”, “}n”, “n”, “mpl.figure.prototype.close_ws = function(fig, msg){n”, ” fig.send_message(‘closing’, msg);n”, ” // fig.ws.close()n”, “}n”, “n”, “mpl.figure.prototype.push_to_output = function(remove_interactive) {n”, ” // Turn the data on the canvas into data in the output cell.n”, ” var width = this.canvas.width/mpl.ration”, ” var dataURL = this.canvas.toDataURL();n”, ” this.cell_info[1][‘text/html’] = ‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’;n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Tell IPython that the notebook contents must change.n”, ” IPython.notebook.set_dirty(true);n”, ” this.send_message(“ack”, {});n”, ” var fig = this;n”, ” // Wait a second, then push the new image to the DOM son”, ” // that it is saved nicely (might be nice to debounce this).n”, ” setTimeout(function () { fig.push_to_output() }, 1000);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items){n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) { continue; };n”, “n”, ” var button = $(‘<button class=”btn btn-default” href=”#” title=”’ + name + ‘”><i class=”fa ‘ + image + ‘ fa-lg”></i></button>’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, ” nav_element.append(button);n”, ” }n”, “n”, ” // Add the status bar.n”, ” var status_bar = $(‘<span class=”mpl-message” style=”text-align:right; float: right;”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “n”, ” // Add the close button to the window.n”, ” var buttongrp = $(‘<div class=”btn-group inline pull-right”></div>’);n”, ” var button = $(‘<button class=”btn btn-mini btn-primary” href=”#” title=”Stop Interaction”><i class=”fa fa-power-off icon-remove icon-large”></i></button>’);n”, ” button.click(function (evt) { fig.handle_close(fig, {}); } );n”, ” button.mouseover(‘Stop Interaction’, toolbar_mouse_event);n”, ” buttongrp.append(button);n”, ” var titlebar = this.root.find($(‘.ui-dialog-titlebar’));n”, ” titlebar.prepend(buttongrp);n”, “}n”, “n”, “mpl.figure.prototype._root_extra_style = function(el){n”, ” var fig = thisn”, ” el.on(“remove”, function(){n”, “tfig.close_ws(fig, {});n”, ” });n”, “}n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(el){n”, ” // this is important to make the div ‘focusablen”, ” el.attr(‘tabindex’, 0)n”, ” // reach out to IPython and tell the keyboard manager to turn it’s selfn”, ” // off when our div gets focusn”, “n”, ” // location in version 3n”, ” if (IPython.notebook.keyboard_manager) {n”, ” IPython.notebook.keyboard_manager.register_events(el);n”, ” }n”, ” else {n”, ” // location in version 2n”, ” IPython.keyboard_manager.register_events(el);n”, ” }n”, “n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” var manager = IPython.notebook.keyboard_manager;n”, ” if (!manager)n”, ” manager = IPython.keyboard_manager;n”, “n”, ” // Check for shift+entern”, ” if (event.shiftKey && event.which == 13) {n”, ” this.canvas_div.blur();n”, ” event.shiftKey = false;n”, ” // Send a “J” for go to next celln”, ” event.which = 74;n”, ” event.keyCode = 74;n”, ” manager.command_mode();n”, ” manager.handle_keydown(event);n”, ” }n”, “}n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” fig.ondownload(fig, null);n”, “}n”, “n”, “n”, “mpl.find_output_cell = function(html_output) {n”, ” // Return the cell and output element which can be found *uniquely in the notebook.n”, ” // Note - this is a bit hacky, but it is done because the “notebook_saving.Notebook”n”, ” // IPython event is triggered only after the cells have been serialised, which forn”, ” // our purposes (turning an active figure into a static one), is too late.n”, ” var cells = IPython.notebook.get_cells();n”, ” var ncells = cells.length;n”, ” for (var i=0; i<ncells; i++) {n”, ” var cell = cells[i];n”, ” if (cell.cell_type === ‘code’){n”, ” for (var j=0; j<cell.output_area.outputs.length; j++) {n”, ” var data = cell.output_area.outputs[j];n”, ” if (data.data) {n”, ” // IPython >= 3 moved mimebundle to data attribute of outputn”, ” data = data.data;n”, ” }n”, ” if (data[‘text/html’] == html_output) {n”, ” return [cell, data, j];n”, ” }n”, ” }n”, ” }n”, ” }n”, “}n”, “n”, “// Register the function which deals with the matplotlib target/channel.n”, “// The kernel may be null if the page has been refreshed.n”, “if (IPython.notebook.kernel != null) {n”, ” IPython.notebook.kernel.comm_manager.register_target(‘matplotlib’, mpl.mpl_figure_comm);n”, “}n”

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” width=”864”>”

], “text/plain”: [

“<IPython.core.display.HTML object>”

]

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

}

], “source”: [

“sensors_to_plot = [611, 977, 1772, 4150]n”, “timestamps = processed.Spike_Explorer.get_spike_timestamps_at_sensors(sensors_to_plot)n”, “plt.figure(figsize=(12,5))n”, “_ = plt.eventplot(timestamps.values(), linelengths=0.8, colors=’k’)n”, “_ = plt.xlabel(‘Time (microsecond)’)n”, “_ = plt.ylabel(‘Sensor IDs’)n”, “_ = plt.yticks(labels=[str(s) for s in timestamps.keys()], ticks=range(len(sensors_to_plot)))n”, “_ = plt.title(‘Raster plot of spike timestamps’)”

]

}, {

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

“#### Spike Countsn”, “n”, “Another option is to plot the spike counts for all sensors… the lighter, the more spikes were detected on the sensor:”

]

}, {

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

{

“data”: {

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‘ +n”, ” ‘text-align: center; padding: 3px;”/>’);n”, ” titlebar.append(titletext)n”, ” this.root.append(titlebar);n”, ” this.header = titletext[0];n”, “}n”, “n”, “n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “n”, “mpl.figure.prototype._root_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “mpl.figure.prototype._init_canvas = function() {n”, ” var fig = this;n”, “n”, ” var canvas_div = $(‘<div/>’);n”, “n”, ” canvas_div.attr(‘style’, ‘position: relative; clear: both; outline: 0’);n”, “n”, ” function canvas_keyboard_event(event) {n”, ” return fig.key_event(event, event[‘data’]);n”, ” }n”, “n”, ” canvas_div.keydown(‘key_press’, canvas_keyboard_event);n”, ” canvas_div.keyup(‘key_release’, canvas_keyboard_event);n”, ” this.canvas_div = canvas_divn”, ” this._canvas_extra_style(canvas_div)n”, ” this.root.append(canvas_div);n”, “n”, ” var canvas = $(‘<canvas/>’);n”, ” canvas.addClass(‘mpl-canvas’);n”, ” canvas.attr(‘style’, “left: 0; top: 0; z-index: 0; outline: 0”)n”, “n”, ” this.canvas = canvas[0];n”, ” this.context = canvas[0].getContext(“2d”);n”, “n”, ” var backingStore = this.context.backingStorePixelRatio ||n”, “tthis.context.webkitBackingStorePixelRatio ||n”, “tthis.context.mozBackingStorePixelRatio ||n”, “tthis.context.msBackingStorePixelRatio ||n”, “tthis.context.oBackingStorePixelRatio ||n”, “tthis.context.backingStorePixelRatio || 1;n”, “n”, ” mpl.ratio = (window.devicePixelRatio || 1) / backingStore;n”, “n”, ” var rubberband = $(‘<canvas/>’);n”, ” rubberband.attr(‘style’, “position: absolute; left: 0; top: 0; z-index: 1;”)n”, “n”, ” var pass_mouse_events = true;n”, “n”, ” canvas_div.resizable({n”, ” start: function(event, ui) {n”, ” pass_mouse_events = false;n”, ” },n”, ” resize: function(event, ui) {n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” stop: function(event, ui) {n”, ” pass_mouse_events = true;n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” });n”, “n”, ” function mouse_event_fn(event) {n”, ” if (pass_mouse_events)n”, ” return fig.mouse_event(event, event[‘data’]);n”, ” }n”, “n”, ” rubberband.mousedown(‘button_press’, mouse_event_fn);n”, ” rubberband.mouseup(‘button_release’, mouse_event_fn);n”, ” // Throttle sequential mouse events to 1 every 20ms.n”, ” rubberband.mousemove(‘motion_notify’, mouse_event_fn);n”, “n”, ” rubberband.mouseenter(‘figure_enter’, mouse_event_fn);n”, ” rubberband.mouseleave(‘figure_leave’, mouse_event_fn);n”, “n”, ” canvas_div.on(“wheel”, function (event) {n”, ” event = event.originalEvent;n”, ” event[‘data’] = ‘scroll’n”, ” if (event.deltaY < 0) {n”, ” event.step = 1;n”, ” } else {n”, ” event.step = -1;n”, ” }n”, ” mouse_event_fn(event);n”, ” });n”, “n”, ” canvas_div.append(canvas);n”, ” canvas_div.append(rubberband);n”, “n”, ” this.rubberband = rubberband;n”, ” this.rubberband_canvas = rubberband[0];n”, ” this.rubberband_context = rubberband[0].getContext(“2d”);n”, ” this.rubberband_context.strokeStyle = “#000000”;n”, “n”, ” this._resize_canvas = function(width, height) {n”, ” // Keep the size of the canvas, canvas container, and rubber bandn”, ” // canvas in synch.n”, ” canvas_div.css(‘width’, width)n”, ” canvas_div.css(‘height’, height)n”, “n”, ” canvas.attr(‘width’, width * mpl.ratio);n”, ” canvas.attr(‘height’, height * mpl.ratio);n”, ” canvas.attr(‘style’, ‘width: ‘ + width + ‘px; height: ‘ + height + ‘px;’);n”, “n”, ” rubberband.attr(‘width’, width);n”, ” rubberband.attr(‘height’, height);n”, ” }n”, “n”, ” // Set the figure to an initial 600x600px, this will subsequently be updatedn”, ” // upon first draw.n”, ” this._resize_canvas(600, 600);n”, “n”, ” // Disable right mouse context menu.n”, ” $(this.rubberband_canvas).bind(“contextmenu”,function(e){n”, ” return false;n”, ” });n”, “n”, ” function set_focus () {n”, ” canvas.focus();n”, ” canvas_div.focus();n”, ” }n”, “n”, ” window.setTimeout(set_focus, 100);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items) {n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) {n”, ” // put a spacer in here.n”, ” continue;n”, ” }n”, ” var button = $(‘<button/>’);n”, ” button.addClass(‘ui-button ui-widget ui-state-default ui-corner-all ‘ +n”, ” ‘ui-button-icon-only’);n”, ” button.attr(‘role’, ‘button’);n”, ” button.attr(‘aria-disabled’, ‘false’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, “n”, ” var icon_img = $(‘<span/>’);n”, ” icon_img.addClass(‘ui-button-icon-primary ui-icon’);n”, ” icon_img.addClass(image);n”, ” icon_img.addClass(‘ui-corner-all’);n”, “n”, ” var tooltip_span = $(‘<span/>’);n”, ” tooltip_span.addClass(‘ui-button-text’);n”, ” tooltip_span.html(tooltip);n”, “n”, ” button.append(icon_img);n”, ” button.append(tooltip_span);n”, “n”, ” nav_element.append(button);n”, ” }n”, “n”, ” var fmt_picker_span = $(‘<span/>’);n”, “n”, ” var fmt_picker = $(‘<select/>’);n”, ” fmt_picker.addClass(‘mpl-toolbar-option ui-widget ui-widget-content’);n”, ” fmt_picker_span.append(fmt_picker);n”, ” nav_element.append(fmt_picker_span);n”, ” this.format_dropdown = fmt_picker[0];n”, “n”, ” for (var ind in mpl.extensions) {n”, ” var fmt = mpl.extensions[ind];n”, ” var option = $(n”, ” ‘<option/>’, {selected: fmt === mpl.default_extension}).html(fmt);n”, ” fmt_picker.append(option);n”, ” }n”, “n”, ” // Add hover states to the ui-buttonsn”, ” $( “.ui-button” ).hover(n”, ” function() { $(this).addClass(“ui-state-hover”);},n”, ” function() { $(this).removeClass(“ui-state-hover”);}n”, ” );n”, “n”, ” var status_bar = $(‘<span class=”mpl-message”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “}n”, “n”, “mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {n”, ” // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,n”, ” // which will in turn request a refresh of the image.n”, ” this.send_message(‘resize’, {‘width’: x_pixels, ‘height’: y_pixels});n”, “}n”, “n”, “mpl.figure.prototype.send_message = function(type, properties) {n”, ” properties[‘type’] = type;n”, ” properties[‘figure_id’] = this.id;n”, ” this.ws.send(JSON.stringify(properties));n”, “}n”, “n”, “mpl.figure.prototype.send_draw_message = function() {n”, ” if (!this.waiting) {n”, ” this.waiting = true;n”, ” this.ws.send(JSON.stringify({type: “draw”, figure_id: this.id}));n”, ” }n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” var format_dropdown = fig.format_dropdown;n”, ” var format = format_dropdown.options[format_dropdown.selectedIndex].value;n”, ” fig.ondownload(fig, format);n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_resize = function(fig, msg) {n”, ” var size = msg[‘size’];n”, ” if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {n”, ” fig._resize_canvas(size[0], size[1]);n”, ” fig.send_message(“refresh”, {});n”, ” };n”, “}n”, “n”, “mpl.figure.prototype.handle_rubberband = function(fig, msg) {n”, ” var x0 = msg[‘x0’] / mpl.ratio;n”, ” var y0 = (fig.canvas.height - msg[‘y0’]) / mpl.ratio;n”, ” var x1 = msg[‘x1’] / mpl.ratio;n”, ” var y1 = (fig.canvas.height - msg[‘y1’]) / mpl.ratio;n”, ” x0 = Math.floor(x0) + 0.5;n”, ” y0 = Math.floor(y0) + 0.5;n”, ” x1 = Math.floor(x1) + 0.5;n”, ” y1 = Math.floor(y1) + 0.5;n”, ” var min_x = Math.min(x0, x1);n”, ” var min_y = Math.min(y0, y1);n”, ” var width = Math.abs(x1 - x0);n”, ” var height = Math.abs(y1 - y0);n”, “n”, ” fig.rubberband_context.clearRect(n”, ” 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);n”, “n”, ” fig.rubberband_context.strokeRect(min_x, min_y, width, height);n”, “}n”, “n”, “mpl.figure.prototype.handle_figure_label = function(fig, msg) {n”, ” // Updates the figure title.n”, ” fig.header.textContent = msg[‘label’];n”, “}n”, “n”, “mpl.figure.prototype.handle_cursor = function(fig, msg) {n”, ” var cursor = msg[‘cursor’];n”, ” switch(cursor)n”, ” {n”, ” case 0:n”, ” cursor = ‘pointer’;n”, ” break;n”, ” case 1:n”, ” cursor = ‘default’;n”, ” break;n”, ” case 2:n”, ” cursor = ‘crosshair’;n”, ” break;n”, ” case 3:n”, ” cursor = ‘move’;n”, ” break;n”, ” }n”, ” fig.rubberband_canvas.style.cursor = cursor;n”, “}n”, “n”, “mpl.figure.prototype.handle_message = function(fig, msg) {n”, ” fig.message.textContent = msg[‘message’];n”, “}n”, “n”, “mpl.figure.prototype.handle_draw = function(fig, msg) {n”, ” // Request the server to send over a new figure.n”, ” fig.send_draw_message();n”, “}n”, “n”, “mpl.figure.prototype.handle_image_mode = function(fig, msg) {n”, ” fig.image_mode = msg[‘mode’];n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Called whenever the canvas gets updated.n”, ” this.send_message(“ack”, {});n”, “}n”, “n”, “// A function to construct a web socket function for onmessage handling.n”, “// Called in the figure constructor.n”, “mpl.figure.prototype._make_on_message_function = function(fig) {n”, ” return function socket_on_message(evt) {n”, ” if (evt.data instanceof Blob) {n”, ” / FIXME: We get “Resource interpreted as Image butn”, ” * transferred with MIME type text/plain:” errors onn”, ” * Chrome. But how to set the MIME type? It doesn’t seemn”, ” * to be part of the websocket stream /n”, ” evt.data.type = “image/png”;n”, “n”, ” / Free the memory for the previous frames /n”, ” if (fig.imageObj.src) {n”, ” (window.URL || window.webkitURL).revokeObjectURL(n”, ” fig.imageObj.src);n”, ” }n”, “n”, ” fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(n”, ” evt.data);n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, ” else if (typeof evt.data === ‘string’ && evt.data.slice(0, 21) == “data:image/png;base64”) {n”, ” fig.imageObj.src = evt.data;n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, “n”, ” var msg = JSON.parse(evt.data);n”, ” var msg_type = msg[‘type’];n”, “n”, ” // Call the “handle_{type}” callback, which takesn”, ” // the figure and JSON message as its only arguments.n”, ” try {n”, ” var callback = fig[“handle_” + msg_type];n”, ” } catch (e) {n”, ” console.log(“No handler for the ‘” + msg_type + “’ message type: “, msg);n”, ” return;n”, ” }n”, “n”, ” if (callback) {n”, ” try {n”, ” // console.log(“Handling ‘” + msg_type + “’ message: “, msg);n”, ” callback(fig, msg);n”, ” } catch (e) {n”, ” console.log(“Exception inside the ‘handler_” + msg_type + “’ callback:”, e, e.stack, msg);n”, ” }n”, ” }n”, ” };n”, “}n”, “n”, “// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvasn”, “mpl.findpos = function(e) {n”, ” //this section is from http://www.quirksmode.org/js/events_properties.htmln”, ” var targ;n”, ” if (!e)n”, ” e = window.event;n”, ” if (e.target)n”, ” targ = e.target;n”, ” else if (e.srcElement)n”, ” targ = e.srcElement;n”, ” if (targ.nodeType == 3) // defeat Safari bugn”, ” targ = targ.parentNode;n”, “n”, ” // jQuery normalizes the pageX and pageYn”, ” // pageX,Y are the mouse positions relative to the documentn”, ” // offset() returns the position of the element relative to the documentn”, ” var x = e.pageX - $(targ).offset().left;n”, ” var y = e.pageY - $(targ).offset().top;n”, “n”, ” return {“x”: x, “y”: y};n”, “};n”, “n”, “/n”, ” * return a copy of an object with only non-object keysn”, ” * we need this to avoid circular referencesn”, ” * http://stackoverflow.com/a/24161582/3208463n”, ” /n”, “function simpleKeys (original) {n”, ” return Object.keys(original).reduce(function (obj, key) {n”, ” if (typeof original[key] !== ‘object’)n”, ” obj[key] = original[key]n”, ” return obj;n”, ” }, {});n”, “}n”, “n”, “mpl.figure.prototype.mouse_event = function(event, name) {n”, ” var canvas_pos = mpl.findpos(event)n”, “n”, ” if (name === ‘button_press’)n”, ” {n”, ” this.canvas.focus();n”, ” this.canvas_div.focus();n”, ” }n”, “n”, ” var x = canvas_pos.x * mpl.ratio;n”, ” var y = canvas_pos.y * mpl.ratio;n”, “n”, ” this.send_message(name, {x: x, y: y, button: event.button,n”, ” step: event.step,n”, ” guiEvent: simpleKeys(event)});n”, “n”, ” / This prevents the web browser from automatically changing ton”, ” * the text insertion cursor when the button is pressed. We wantn”, ” * to control all of the cursor setting manually through then”, ” * ‘cursor’ event from matplotlib /n”, ” event.preventDefault();n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” // Handle any extra behaviour associated with a key eventn”, “}n”, “n”, “mpl.figure.prototype.key_event = function(event, name) {n”, “n”, ” // Prevent repeat eventsn”, ” if (name == ‘key_press’)n”, ” {n”, ” if (event.which === this._key)n”, ” return;n”, ” elsen”, ” this._key = event.which;n”, ” }n”, ” if (name == ‘key_release’)n”, ” this._key = null;n”, “n”, ” var value = ‘’;n”, ” if (event.ctrlKey && event.which != 17)n”, ” value += “ctrl+”;n”, ” if (event.altKey && event.which != 18)n”, ” value += “alt+”;n”, ” if (event.shiftKey && event.which != 16)n”, ” value += “shift+”;n”, “n”, ” value += ‘k’;n”, ” value += event.which.toString();n”, “n”, ” this._key_event_extra(event, name);n”, “n”, ” this.send_message(name, {key: value,n”, ” guiEvent: simpleKeys(event)});n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype.toolbar_button_onclick = function(name) {n”, ” if (name == ‘download’) {n”, ” this.handle_save(this, null);n”, ” } else {n”, ” this.send_message(“toolbar_button”, {name: name});n”, ” }n”, “};n”, “n”, “mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {n”, ” this.message.textContent = tooltip;n”, “};n”, “mpl.toolbar_items = [[“Home”, “Reset original view”, “fa fa-home icon-home”, “home”], [“Back”, “Back to previous view”, “fa fa-arrow-left icon-arrow-left”, “back”], [“Forward”, “Forward to next view”, “fa fa-arrow-right icon-arrow-right”, “forward”], [“”, “”, “”, “”], [“Pan”, “Pan axes with left mouse, zoom with right”, “fa fa-arrows icon-move”, “pan”], [“Zoom”, “Zoom to rectangle”, “fa fa-square-o icon-check-empty”, “zoom”], [“”, “”, “”, “”], [“Download”, “Download plot”, “fa fa-floppy-o icon-save”, “download”]];n”, “n”, “mpl.extensions = [“eps”, “jpeg”, “pdf”, “png”, “ps”, “raw”, “svg”, “tif”];n”, “n”, “mpl.default_extension = “png”;var comm_websocket_adapter = function(comm) {n”, ” // Create a “websocket”-like object which calls the given IPython commn”, ” // object with the appropriate methods. Currently this is a non binaryn”, ” // socket, so there is still some room for performance tuning.n”, ” var ws = {};n”, “n”, ” ws.close = function() {n”, ” comm.close()n”, ” };n”, ” ws.send = function(m) {n”, ” //console.log(‘sending’, m);n”, ” comm.send(m);n”, ” };n”, ” // Register the callback with on_msg.n”, ” comm.on_msg(function(msg) {n”, ” //console.log(‘receiving’, msg[‘content’][‘data’], msg);n”, ” // Pass the mpl event to the overridden (by mpl) onmessage function.n”, ” ws.onmessage(msg[‘content’][‘data’])n”, ” });n”, ” return ws;n”, “}n”, “n”, “mpl.mpl_figure_comm = function(comm, msg) {n”, ” // This is the function which gets called when the mpl processn”, ” // starts-up an IPython Comm through the “matplotlib” channel.n”, “n”, ” var id = msg.content.data.id;n”, ” // Get hold of the div created by the display call when the Commn”, ” // socket was opened in Python.n”, ” var element = $(“#” + id);n”, ” var ws_proxy = comm_websocket_adapter(comm)n”, “n”, ” function ondownload(figure, format) {n”, ” window.open(figure.imageObj.src);n”, ” }n”, “n”, ” var fig = new mpl.figure(id, ws_proxy,n”, ” ondownload,n”, ” element.get(0));n”, “n”, ” // Call onopen now - mpl needs it, as it is assuming we’ve passed it a realn”, ” // web socket which is closed, not our websocket->open comm proxy.n”, ” ws_proxy.onopen();n”, “n”, ” fig.parent_element = element.get(0);n”, ” fig.cell_info = mpl.find_output_cell(“<div id=’” + id + “’></div>”);n”, ” if (!fig.cell_info) {n”, ” console.error(“Failed to find cell for figure”, id, fig);n”, ” return;n”, ” }n”, “n”, ” var output_index = fig.cell_info[2]n”, ” var cell = fig.cell_info[0];n”, “n”, “};n”, “n”, “mpl.figure.prototype.handle_close = function(fig, msg) {n”, ” var width = fig.canvas.width/mpl.ration”, ” fig.root.unbind(‘remove’)n”, “n”, ” // Update the output cell to use the data from the current canvas.n”, ” fig.push_to_output();n”, ” var dataURL = fig.canvas.toDataURL();n”, ” // Re-enable the keyboard manager in IPython - without this line, in FF,n”, ” // the notebook keyboard shortcuts fail.n”, ” IPython.keyboard_manager.enable()n”, ” $(fig.parent_element).html(‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’);n”, ” fig.close_ws(fig, msg);n”, “}n”, “n”, “mpl.figure.prototype.close_ws = function(fig, msg){n”, ” fig.send_message(‘closing’, msg);n”, ” // fig.ws.close()n”, “}n”, “n”, “mpl.figure.prototype.push_to_output = function(remove_interactive) {n”, ” // Turn the data on the canvas into data in the output cell.n”, ” var width = this.canvas.width/mpl.ration”, ” var dataURL = this.canvas.toDataURL();n”, ” this.cell_info[1][‘text/html’] = ‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’;n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Tell IPython that the notebook contents must change.n”, ” IPython.notebook.set_dirty(true);n”, ” this.send_message(“ack”, {});n”, ” var fig = this;n”, ” // Wait a second, then push the new image to the DOM son”, ” // that it is saved nicely (might be nice to debounce this).n”, ” setTimeout(function () { fig.push_to_output() }, 1000);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items){n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) { continue; };n”, “n”, ” var button = $(‘<button class=”btn btn-default” href=”#” title=”’ + name + ‘”><i class=”fa ‘ + image + ‘ fa-lg”></i></button>’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, ” nav_element.append(button);n”, ” }n”, “n”, ” // Add the status bar.n”, ” var status_bar = $(‘<span class=”mpl-message” style=”text-align:right; float: right;”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “n”, ” // Add the close button to the window.n”, ” var buttongrp = $(‘<div class=”btn-group inline pull-right”></div>’);n”, ” var button = $(‘<button class=”btn btn-mini btn-primary” href=”#” title=”Stop Interaction”><i class=”fa fa-power-off icon-remove icon-large”></i></button>’);n”, ” button.click(function (evt) { fig.handle_close(fig, {}); } );n”, ” button.mouseover(‘Stop Interaction’, toolbar_mouse_event);n”, ” buttongrp.append(button);n”, ” var titlebar = this.root.find($(‘.ui-dialog-titlebar’));n”, ” titlebar.prepend(buttongrp);n”, “}n”, “n”, “mpl.figure.prototype._root_extra_style = function(el){n”, ” var fig = thisn”, ” el.on(“remove”, function(){n”, “tfig.close_ws(fig, {});n”, ” });n”, “}n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(el){n”, ” // this is important to make the div ‘focusablen”, ” el.attr(‘tabindex’, 0)n”, ” // reach out to IPython and tell the keyboard manager to turn it’s selfn”, ” // off when our div gets focusn”, “n”, ” // location in version 3n”, ” if (IPython.notebook.keyboard_manager) {n”, ” IPython.notebook.keyboard_manager.register_events(el);n”, ” }n”, ” else {n”, ” // location in version 2n”, ” IPython.keyboard_manager.register_events(el);n”, ” }n”, “n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” var manager = IPython.notebook.keyboard_manager;n”, ” if (!manager)n”, ” manager = IPython.keyboard_manager;n”, “n”, ” // Check for shift+entern”, ” if (event.shiftKey && event.which == 13) {n”, ” this.canvas_div.blur();n”, ” event.shiftKey = false;n”, ” // Send a “J” for go to next celln”, ” event.which = 74;n”, ” event.keyCode = 74;n”, ” manager.command_mode();n”, ” manager.handle_keydown(event);n”, ” }n”, “}n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” fig.ondownload(fig, null);n”, “}n”, “n”, “n”, “mpl.find_output_cell = function(html_output) {n”, ” // Return the cell and output element which can be found *uniquely in the notebook.n”, ” // Note - this is a bit hacky, but it is done because the “notebook_saving.Notebook”n”, ” // IPython event is triggered only after the cells have been serialised, which forn”, ” // our purposes (turning an active figure into a static one), is too late.n”, ” var cells = IPython.notebook.get_cells();n”, ” var ncells = cells.length;n”, ” for (var i=0; i<ncells; i++) {n”, ” var cell = cells[i];n”, ” if (cell.cell_type === ‘code’){n”, ” for (var j=0; j<cell.output_area.outputs.length; j++) {n”, ” var data = cell.output_area.outputs[j];n”, ” if (data.data) {n”, ” // IPython >= 3 moved mimebundle to data attribute of outputn”, ” data = data.data;n”, ” }n”, ” if (data[‘text/html’] == html_output) {n”, ” return [cell, data, j];n”, ” }n”, ” }n”, ” }n”, ” }n”, “}n”, “n”, “// Register the function which deals with the matplotlib target/channel.n”, “// The kernel may be null if the page has been refreshed.n”, “if (IPython.notebook.kernel != null) {n”, ” IPython.notebook.kernel.comm_manager.register_target(‘matplotlib’, mpl.mpl_figure_comm);n”, “}n”

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” width=”720”>”

], “text/plain”: [

“<IPython.core.display.HTML object>”

]

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

}

], “source”: [

“ids_with_spikes, spike_counts = np.unique(processed.Spike_Explorer.SpikeData[‘SensorID’], return_counts=True)n”, “sensor_counts = np.zeros((65, 65))n”, “for i in range(len(ids_with_spikes)):n”, ” sensor = ids_with_spikes[i]n”, ” count = spike_counts[i]n”, ” row,col = (sensor-1) % 65, (sensor-1) // 65n”, ” sensor_counts[row,col] = countn”, “n”, “plt.figure(figsize=(10,10))n”, “_ = plt.imshow(sensor_counts)n”, “plt.set_cmap(“gray”)n”, “plt.title(‘Spike Counts’)n”, “plt.show()”

]

}, {

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

“#### Spike Cutoutsn”, “n”, “Finally, we can also check out an overlay of the spike cutouts of a sensor:”

]

}, {

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

{

“data”: {

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“/* Put everything inside the global mpl namespace /n”, “window.mpl = {};n”, “n”, “n”, “mpl.get_websocket_type = function() {n”, ” if (typeof(WebSocket) !== ‘undefined’) {n”, ” return WebSocket;n”, ” } else if (typeof(MozWebSocket) !== ‘undefined’) {n”, ” return MozWebSocket;n”, ” } else {n”, ” alert(‘Your browser does not have WebSocket support. ‘ +n”, ” ‘Please try Chrome, Safari or Firefox ≥ 6. ‘ +n”, ” ‘Firefox 4 and 5 are also supported but you ‘ +n”, ” ‘have to enable WebSockets in about:config.’);n”, ” };n”, “}n”, “n”, “mpl.figure = function(figure_id, websocket, ondownload, parent_element) {n”, ” this.id = figure_id;n”, “n”, ” this.ws = websocket;n”, “n”, ” this.supports_binary = (this.ws.binaryType != undefined);n”, “n”, ” if (!this.supports_binary) {n”, ” var warnings = document.getElementById(“mpl-warnings”);n”, ” if (warnings) {n”, ” warnings.style.display = ‘block’;n”, ” warnings.textContent = (n”, ” “This browser does not support binary websocket messages. ” +n”, ” “Performance may be slow.”);n”, ” }n”, ” }n”, “n”, ” this.imageObj = new Image();n”, “n”, ” this.context = undefined;n”, ” this.message = undefined;n”, ” this.canvas = undefined;n”, ” this.rubberband_canvas = undefined;n”, ” this.rubberband_context = undefined;n”, ” this.format_dropdown = undefined;n”, “n”, ” this.image_mode = ‘full’;n”, “n”, ” this.root = $(‘<div/>’);n”, ” this._root_extra_style(this.root)n”, ” this.root.attr(‘style’, ‘display: inline-block’);n”, “n”, ” $(parent_element).append(this.root);n”, “n”, ” this._init_header(this);n”, ” this._init_canvas(this);n”, ” this._init_toolbar(this);n”, “n”, ” var fig = this;n”, “n”, ” this.waiting = false;n”, “n”, ” this.ws.onopen = function () {n”, ” fig.send_message(“supports_binary”, {value: fig.supports_binary});n”, ” fig.send_message(“send_image_mode”, {});n”, ” if (mpl.ratio != 1) {n”, ” fig.send_message(“set_dpi_ratio”, {‘dpi_ratio’: mpl.ratio});n”, ” }n”, ” fig.send_message(“refresh”, {});n”, ” }n”, “n”, ” this.imageObj.onload = function() {n”, ” if (fig.image_mode == ‘full’) {n”, ” // Full images could contain transparency (where diff imagesn”, ” // almost always do), so we need to clear the canvas so thatn”, ” // there is no ghosting.n”, ” fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);n”, ” }n”, ” fig.context.drawImage(fig.imageObj, 0, 0);n”, ” };n”, “n”, ” this.imageObj.onunload = function() {n”, ” fig.ws.close();n”, ” }n”, “n”, ” this.ws.onmessage = this._make_on_message_function(this);n”, “n”, ” this.ondownload = ondownload;n”, “}n”, “n”, “mpl.figure.prototype._init_header = function() {n”, ” var titlebar = $(n”, ” ‘<div class=”ui-dialog-titlebar ui-widget-header ui-corner-all ‘ +n”, ” ‘ui-helper-clearfix”/>’);n”, ” var titletext = $(n”, ” ‘<div class=”ui-dialog-title” style=”width: 100%; ‘ +n”, ” ‘text-align: center; padding: 3px;”/>’);n”, ” titlebar.append(titletext)n”, ” this.root.append(titlebar);n”, ” this.header = titletext[0];n”, “}n”, “n”, “n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “n”, “mpl.figure.prototype._root_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “mpl.figure.prototype._init_canvas = function() {n”, ” var fig = this;n”, “n”, ” var canvas_div = $(‘<div/>’);n”, “n”, ” canvas_div.attr(‘style’, ‘position: relative; clear: both; outline: 0’);n”, “n”, ” function canvas_keyboard_event(event) {n”, ” return fig.key_event(event, event[‘data’]);n”, ” }n”, “n”, ” canvas_div.keydown(‘key_press’, canvas_keyboard_event);n”, ” canvas_div.keyup(‘key_release’, canvas_keyboard_event);n”, ” this.canvas_div = canvas_divn”, ” this._canvas_extra_style(canvas_div)n”, ” this.root.append(canvas_div);n”, “n”, ” var canvas = $(‘<canvas/>’);n”, ” canvas.addClass(‘mpl-canvas’);n”, ” canvas.attr(‘style’, “left: 0; top: 0; z-index: 0; outline: 0”)n”, “n”, ” this.canvas = canvas[0];n”, ” this.context = canvas[0].getContext(“2d”);n”, “n”, ” var backingStore = this.context.backingStorePixelRatio ||n”, “tthis.context.webkitBackingStorePixelRatio ||n”, “tthis.context.mozBackingStorePixelRatio ||n”, “tthis.context.msBackingStorePixelRatio ||n”, “tthis.context.oBackingStorePixelRatio ||n”, “tthis.context.backingStorePixelRatio || 1;n”, “n”, ” mpl.ratio = (window.devicePixelRatio || 1) / backingStore;n”, “n”, ” var rubberband = $(‘<canvas/>’);n”, ” rubberband.attr(‘style’, “position: absolute; left: 0; top: 0; z-index: 1;”)n”, “n”, ” var pass_mouse_events = true;n”, “n”, ” canvas_div.resizable({n”, ” start: function(event, ui) {n”, ” pass_mouse_events = false;n”, ” },n”, ” resize: function(event, ui) {n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” stop: function(event, ui) {n”, ” pass_mouse_events = true;n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” });n”, “n”, ” function mouse_event_fn(event) {n”, ” if (pass_mouse_events)n”, ” return fig.mouse_event(event, event[‘data’]);n”, ” }n”, “n”, ” rubberband.mousedown(‘button_press’, mouse_event_fn);n”, ” rubberband.mouseup(‘button_release’, mouse_event_fn);n”, ” // Throttle sequential mouse events to 1 every 20ms.n”, ” rubberband.mousemove(‘motion_notify’, mouse_event_fn);n”, “n”, ” rubberband.mouseenter(‘figure_enter’, mouse_event_fn);n”, ” rubberband.mouseleave(‘figure_leave’, mouse_event_fn);n”, “n”, ” canvas_div.on(“wheel”, function (event) {n”, ” event = event.originalEvent;n”, ” event[‘data’] = ‘scroll’n”, ” if (event.deltaY < 0) {n”, ” event.step = 1;n”, ” } else {n”, ” event.step = -1;n”, ” }n”, ” mouse_event_fn(event);n”, ” });n”, “n”, ” canvas_div.append(canvas);n”, ” canvas_div.append(rubberband);n”, “n”, ” this.rubberband = rubberband;n”, ” this.rubberband_canvas = rubberband[0];n”, ” this.rubberband_context = rubberband[0].getContext(“2d”);n”, ” this.rubberband_context.strokeStyle = “#000000”;n”, “n”, ” this._resize_canvas = function(width, height) {n”, ” // Keep the size of the canvas, canvas container, and rubber bandn”, ” // canvas in synch.n”, ” canvas_div.css(‘width’, width)n”, ” canvas_div.css(‘height’, height)n”, “n”, ” canvas.attr(‘width’, width * mpl.ratio);n”, ” canvas.attr(‘height’, height * mpl.ratio);n”, ” canvas.attr(‘style’, ‘width: ‘ + width + ‘px; height: ‘ + height + ‘px;’);n”, “n”, ” rubberband.attr(‘width’, width);n”, ” rubberband.attr(‘height’, height);n”, ” }n”, “n”, ” // Set the figure to an initial 600x600px, this will subsequently be updatedn”, ” // upon first draw.n”, ” this._resize_canvas(600, 600);n”, “n”, ” // Disable right mouse context menu.n”, ” $(this.rubberband_canvas).bind(“contextmenu”,function(e){n”, ” return false;n”, ” });n”, “n”, ” function set_focus () {n”, ” canvas.focus();n”, ” canvas_div.focus();n”, ” }n”, “n”, ” window.setTimeout(set_focus, 100);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items) {n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) {n”, ” // put a spacer in here.n”, ” continue;n”, ” }n”, ” var button = $(‘<button/>’);n”, ” button.addClass(‘ui-button ui-widget ui-state-default ui-corner-all ‘ +n”, ” ‘ui-button-icon-only’);n”, ” button.attr(‘role’, ‘button’);n”, ” button.attr(‘aria-disabled’, ‘false’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, “n”, ” var icon_img = $(‘<span/>’);n”, ” icon_img.addClass(‘ui-button-icon-primary ui-icon’);n”, ” icon_img.addClass(image);n”, ” icon_img.addClass(‘ui-corner-all’);n”, “n”, ” var tooltip_span = $(‘<span/>’);n”, ” tooltip_span.addClass(‘ui-button-text’);n”, ” tooltip_span.html(tooltip);n”, “n”, ” button.append(icon_img);n”, ” button.append(tooltip_span);n”, “n”, ” nav_element.append(button);n”, ” }n”, “n”, ” var fmt_picker_span = $(‘<span/>’);n”, “n”, ” var fmt_picker = $(‘<select/>’);n”, ” fmt_picker.addClass(‘mpl-toolbar-option ui-widget ui-widget-content’);n”, ” fmt_picker_span.append(fmt_picker);n”, ” nav_element.append(fmt_picker_span);n”, ” this.format_dropdown = fmt_picker[0];n”, “n”, ” for (var ind in mpl.extensions) {n”, ” var fmt = mpl.extensions[ind];n”, ” var option = $(n”, ” ‘<option/>’, {selected: fmt === mpl.default_extension}).html(fmt);n”, ” fmt_picker.append(option);n”, ” }n”, “n”, ” // Add hover states to the ui-buttonsn”, ” $( “.ui-button” ).hover(n”, ” function() { $(this).addClass(“ui-state-hover”);},n”, ” function() { $(this).removeClass(“ui-state-hover”);}n”, ” );n”, “n”, ” var status_bar = $(‘<span class=”mpl-message”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “}n”, “n”, “mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {n”, ” // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,n”, ” // which will in turn request a refresh of the image.n”, ” this.send_message(‘resize’, {‘width’: x_pixels, ‘height’: y_pixels});n”, “}n”, “n”, “mpl.figure.prototype.send_message = function(type, properties) {n”, ” properties[‘type’] = type;n”, ” properties[‘figure_id’] = this.id;n”, ” this.ws.send(JSON.stringify(properties));n”, “}n”, “n”, “mpl.figure.prototype.send_draw_message = function() {n”, ” if (!this.waiting) {n”, ” this.waiting = true;n”, ” this.ws.send(JSON.stringify({type: “draw”, figure_id: this.id}));n”, ” }n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” var format_dropdown = fig.format_dropdown;n”, ” var format = format_dropdown.options[format_dropdown.selectedIndex].value;n”, ” fig.ondownload(fig, format);n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_resize = function(fig, msg) {n”, ” var size = msg[‘size’];n”, ” if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {n”, ” fig._resize_canvas(size[0], size[1]);n”, ” fig.send_message(“refresh”, {});n”, ” };n”, “}n”, “n”, “mpl.figure.prototype.handle_rubberband = function(fig, msg) {n”, ” var x0 = msg[‘x0’] / mpl.ratio;n”, ” var y0 = (fig.canvas.height - msg[‘y0’]) / mpl.ratio;n”, ” var x1 = msg[‘x1’] / mpl.ratio;n”, ” var y1 = (fig.canvas.height - msg[‘y1’]) / mpl.ratio;n”, ” x0 = Math.floor(x0) + 0.5;n”, ” y0 = Math.floor(y0) + 0.5;n”, ” x1 = Math.floor(x1) + 0.5;n”, ” y1 = Math.floor(y1) + 0.5;n”, ” var min_x = Math.min(x0, x1);n”, ” var min_y = Math.min(y0, y1);n”, ” var width = Math.abs(x1 - x0);n”, ” var height = Math.abs(y1 - y0);n”, “n”, ” fig.rubberband_context.clearRect(n”, ” 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);n”, “n”, ” fig.rubberband_context.strokeRect(min_x, min_y, width, height);n”, “}n”, “n”, “mpl.figure.prototype.handle_figure_label = function(fig, msg) {n”, ” // Updates the figure title.n”, ” fig.header.textContent = msg[‘label’];n”, “}n”, “n”, “mpl.figure.prototype.handle_cursor = function(fig, msg) {n”, ” var cursor = msg[‘cursor’];n”, ” switch(cursor)n”, ” {n”, ” case 0:n”, ” cursor = ‘pointer’;n”, ” break;n”, ” case 1:n”, ” cursor = ‘default’;n”, ” break;n”, ” case 2:n”, ” cursor = ‘crosshair’;n”, ” break;n”, ” case 3:n”, ” cursor = ‘move’;n”, ” break;n”, ” }n”, ” fig.rubberband_canvas.style.cursor = cursor;n”, “}n”, “n”, “mpl.figure.prototype.handle_message = function(fig, msg) {n”, ” fig.message.textContent = msg[‘message’];n”, “}n”, “n”, “mpl.figure.prototype.handle_draw = function(fig, msg) {n”, ” // Request the server to send over a new figure.n”, ” fig.send_draw_message();n”, “}n”, “n”, “mpl.figure.prototype.handle_image_mode = function(fig, msg) {n”, ” fig.image_mode = msg[‘mode’];n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Called whenever the canvas gets updated.n”, ” this.send_message(“ack”, {});n”, “}n”, “n”, “// A function to construct a web socket function for onmessage handling.n”, “// Called in the figure constructor.n”, “mpl.figure.prototype._make_on_message_function = function(fig) {n”, ” return function socket_on_message(evt) {n”, ” if (evt.data instanceof Blob) {n”, ” / FIXME: We get “Resource interpreted as Image butn”, ” * transferred with MIME type text/plain:” errors onn”, ” * Chrome. But how to set the MIME type? It doesn’t seemn”, ” * to be part of the websocket stream /n”, ” evt.data.type = “image/png”;n”, “n”, ” / Free the memory for the previous frames /n”, ” if (fig.imageObj.src) {n”, ” (window.URL || window.webkitURL).revokeObjectURL(n”, ” fig.imageObj.src);n”, ” }n”, “n”, ” fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(n”, ” evt.data);n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, ” else if (typeof evt.data === ‘string’ && evt.data.slice(0, 21) == “data:image/png;base64”) {n”, ” fig.imageObj.src = evt.data;n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, “n”, ” var msg = JSON.parse(evt.data);n”, ” var msg_type = msg[‘type’];n”, “n”, ” // Call the “handle_{type}” callback, which takesn”, ” // the figure and JSON message as its only arguments.n”, ” try {n”, ” var callback = fig[“handle_” + msg_type];n”, ” } catch (e) {n”, ” console.log(“No handler for the ‘” + msg_type + “’ message type: “, msg);n”, ” return;n”, ” }n”, “n”, ” if (callback) {n”, ” try {n”, ” // console.log(“Handling ‘” + msg_type + “’ message: “, msg);n”, ” callback(fig, msg);n”, ” } catch (e) {n”, ” console.log(“Exception inside the ‘handler_” + msg_type + “’ callback:”, e, e.stack, msg);n”, ” }n”, ” }n”, ” };n”, “}n”, “n”, “// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvasn”, “mpl.findpos = function(e) {n”, ” //this section is from http://www.quirksmode.org/js/events_properties.htmln”, ” var targ;n”, ” if (!e)n”, ” e = window.event;n”, ” if (e.target)n”, ” targ = e.target;n”, ” else if (e.srcElement)n”, ” targ = e.srcElement;n”, ” if (targ.nodeType == 3) // defeat Safari bugn”, ” targ = targ.parentNode;n”, “n”, ” // jQuery normalizes the pageX and pageYn”, ” // pageX,Y are the mouse positions relative to the documentn”, ” // offset() returns the position of the element relative to the documentn”, ” var x = e.pageX - $(targ).offset().left;n”, ” var y = e.pageY - $(targ).offset().top;n”, “n”, ” return {“x”: x, “y”: y};n”, “};n”, “n”, “/n”, ” * return a copy of an object with only non-object keysn”, ” * we need this to avoid circular referencesn”, ” * http://stackoverflow.com/a/24161582/3208463n”, ” /n”, “function simpleKeys (original) {n”, ” return Object.keys(original).reduce(function (obj, key) {n”, ” if (typeof original[key] !== ‘object’)n”, ” obj[key] = original[key]n”, ” return obj;n”, ” }, {});n”, “}n”, “n”, “mpl.figure.prototype.mouse_event = function(event, name) {n”, ” var canvas_pos = mpl.findpos(event)n”, “n”, ” if (name === ‘button_press’)n”, ” {n”, ” this.canvas.focus();n”, ” this.canvas_div.focus();n”, ” }n”, “n”, ” var x = canvas_pos.x * mpl.ratio;n”, ” var y = canvas_pos.y * mpl.ratio;n”, “n”, ” this.send_message(name, {x: x, y: y, button: event.button,n”, ” step: event.step,n”, ” guiEvent: simpleKeys(event)});n”, “n”, ” / This prevents the web browser from automatically changing ton”, ” * the text insertion cursor when the button is pressed. We wantn”, ” * to control all of the cursor setting manually through then”, ” * ‘cursor’ event from matplotlib /n”, ” event.preventDefault();n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” // Handle any extra behaviour associated with a key eventn”, “}n”, “n”, “mpl.figure.prototype.key_event = function(event, name) {n”, “n”, ” // Prevent repeat eventsn”, ” if (name == ‘key_press’)n”, ” {n”, ” if (event.which === this._key)n”, ” return;n”, ” elsen”, ” this._key = event.which;n”, ” }n”, ” if (name == ‘key_release’)n”, ” this._key = null;n”, “n”, ” var value = ‘’;n”, ” if (event.ctrlKey && event.which != 17)n”, ” value += “ctrl+”;n”, ” if (event.altKey && event.which != 18)n”, ” value += “alt+”;n”, ” if (event.shiftKey && event.which != 16)n”, ” value += “shift+”;n”, “n”, ” value += ‘k’;n”, ” value += event.which.toString();n”, “n”, ” this._key_event_extra(event, name);n”, “n”, ” this.send_message(name, {key: value,n”, ” guiEvent: simpleKeys(event)});n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype.toolbar_button_onclick = function(name) {n”, ” if (name == ‘download’) {n”, ” this.handle_save(this, null);n”, ” } else {n”, ” this.send_message(“toolbar_button”, {name: name});n”, ” }n”, “};n”, “n”, “mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {n”, ” this.message.textContent = tooltip;n”, “};n”, “mpl.toolbar_items = [[“Home”, “Reset original view”, “fa fa-home icon-home”, “home”], [“Back”, “Back to previous view”, “fa fa-arrow-left icon-arrow-left”, “back”], [“Forward”, “Forward to next view”, “fa fa-arrow-right icon-arrow-right”, “forward”], [“”, “”, “”, “”], [“Pan”, “Pan axes with left mouse, zoom with right”, “fa fa-arrows icon-move”, “pan”], [“Zoom”, “Zoom to rectangle”, “fa fa-square-o icon-check-empty”, “zoom”], [“”, “”, “”, “”], [“Download”, “Download plot”, “fa fa-floppy-o icon-save”, “download”]];n”, “n”, “mpl.extensions = [“eps”, “jpeg”, “pdf”, “png”, “ps”, “raw”, “svg”, “tif”];n”, “n”, “mpl.default_extension = “png”;var comm_websocket_adapter = function(comm) {n”, ” // Create a “websocket”-like object which calls the given IPython commn”, ” // object with the appropriate methods. Currently this is a non binaryn”, ” // socket, so there is still some room for performance tuning.n”, ” var ws = {};n”, “n”, ” ws.close = function() {n”, ” comm.close()n”, ” };n”, ” ws.send = function(m) {n”, ” //console.log(‘sending’, m);n”, ” comm.send(m);n”, ” };n”, ” // Register the callback with on_msg.n”, ” comm.on_msg(function(msg) {n”, ” //console.log(‘receiving’, msg[‘content’][‘data’], msg);n”, ” // Pass the mpl event to the overridden (by mpl) onmessage function.n”, ” ws.onmessage(msg[‘content’][‘data’])n”, ” });n”, ” return ws;n”, “}n”, “n”, “mpl.mpl_figure_comm = function(comm, msg) {n”, ” // This is the function which gets called when the mpl processn”, ” // starts-up an IPython Comm through the “matplotlib” channel.n”, “n”, ” var id = msg.content.data.id;n”, ” // Get hold of the div created by the display call when the Commn”, ” // socket was opened in Python.n”, ” var element = $(“#” + id);n”, ” var ws_proxy = comm_websocket_adapter(comm)n”, “n”, ” function ondownload(figure, format) {n”, ” window.open(figure.imageObj.src);n”, ” }n”, “n”, ” var fig = new mpl.figure(id, ws_proxy,n”, ” ondownload,n”, ” element.get(0));n”, “n”, ” // Call onopen now - mpl needs it, as it is assuming we’ve passed it a realn”, ” // web socket which is closed, not our websocket->open comm proxy.n”, ” ws_proxy.onopen();n”, “n”, ” fig.parent_element = element.get(0);n”, ” fig.cell_info = mpl.find_output_cell(“<div id=’” + id + “’></div>”);n”, ” if (!fig.cell_info) {n”, ” console.error(“Failed to find cell for figure”, id, fig);n”, ” return;n”, ” }n”, “n”, ” var output_index = fig.cell_info[2]n”, ” var cell = fig.cell_info[0];n”, “n”, “};n”, “n”, “mpl.figure.prototype.handle_close = function(fig, msg) {n”, ” var width = fig.canvas.width/mpl.ration”, ” fig.root.unbind(‘remove’)n”, “n”, ” // Update the output cell to use the data from the current canvas.n”, ” fig.push_to_output();n”, ” var dataURL = fig.canvas.toDataURL();n”, ” // Re-enable the keyboard manager in IPython - without this line, in FF,n”, ” // the notebook keyboard shortcuts fail.n”, ” IPython.keyboard_manager.enable()n”, ” $(fig.parent_element).html(‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’);n”, ” fig.close_ws(fig, msg);n”, “}n”, “n”, “mpl.figure.prototype.close_ws = function(fig, msg){n”, ” fig.send_message(‘closing’, msg);n”, ” // fig.ws.close()n”, “}n”, “n”, “mpl.figure.prototype.push_to_output = function(remove_interactive) {n”, ” // Turn the data on the canvas into data in the output cell.n”, ” var width = this.canvas.width/mpl.ration”, ” var dataURL = this.canvas.toDataURL();n”, ” this.cell_info[1][‘text/html’] = ‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’;n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Tell IPython that the notebook contents must change.n”, ” IPython.notebook.set_dirty(true);n”, ” this.send_message(“ack”, {});n”, ” var fig = this;n”, ” // Wait a second, then push the new image to the DOM son”, ” // that it is saved nicely (might be nice to debounce this).n”, ” setTimeout(function () { fig.push_to_output() }, 1000);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items){n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) { continue; };n”, “n”, ” var button = $(‘<button class=”btn btn-default” href=”#” title=”’ + name + ‘”><i class=”fa ‘ + image + ‘ fa-lg”></i></button>’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, ” nav_element.append(button);n”, ” }n”, “n”, ” // Add the status bar.n”, ” var status_bar = $(‘<span class=”mpl-message” style=”text-align:right; float: right;”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “n”, ” // Add the close button to the window.n”, ” var buttongrp = $(‘<div class=”btn-group inline pull-right”></div>’);n”, ” var button = $(‘<button class=”btn btn-mini btn-primary” href=”#” title=”Stop Interaction”><i class=”fa fa-power-off icon-remove icon-large”></i></button>’);n”, ” button.click(function (evt) { fig.handle_close(fig, {}); } );n”, ” button.mouseover(‘Stop Interaction’, toolbar_mouse_event);n”, ” buttongrp.append(button);n”, ” var titlebar = this.root.find($(‘.ui-dialog-titlebar’));n”, ” titlebar.prepend(buttongrp);n”, “}n”, “n”, “mpl.figure.prototype._root_extra_style = function(el){n”, ” var fig = thisn”, ” el.on(“remove”, function(){n”, “tfig.close_ws(fig, {});n”, ” });n”, “}n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(el){n”, ” // this is important to make the div ‘focusablen”, ” el.attr(‘tabindex’, 0)n”, ” // reach out to IPython and tell the keyboard manager to turn it’s selfn”, ” // off when our div gets focusn”, “n”, ” // location in version 3n”, ” if (IPython.notebook.keyboard_manager) {n”, ” IPython.notebook.keyboard_manager.register_events(el);n”, ” }n”, ” else {n”, ” // location in version 2n”, ” IPython.keyboard_manager.register_events(el);n”, ” }n”, “n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” var manager = IPython.notebook.keyboard_manager;n”, ” if (!manager)n”, ” manager = IPython.keyboard_manager;n”, “n”, ” // Check for shift+entern”, ” if (event.shiftKey && event.which == 13) {n”, ” this.canvas_div.blur();n”, ” event.shiftKey = false;n”, ” // Send a “J” for go to next celln”, ” event.which = 74;n”, ” event.keyCode = 74;n”, ” manager.command_mode();n”, ” manager.handle_keydown(event);n”, ” }n”, “}n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” fig.ondownload(fig, null);n”, “}n”, “n”, “n”, “mpl.find_output_cell = function(html_output) {n”, ” // Return the cell and output element which can be found *uniquely in the notebook.n”, ” // Note - this is a bit hacky, but it is done because the “notebook_saving.Notebook”n”, ” // IPython event is triggered only after the cells have been serialised, which forn”, ” // our purposes (turning an active figure into a static one), is too late.n”, ” var cells = IPython.notebook.get_cells();n”, ” var ncells = cells.length;n”, ” for (var i=0; i<ncells; i++) {n”, ” var cell = cells[i];n”, ” if (cell.cell_type === ‘code’){n”, ” for (var j=0; j<cell.output_area.outputs.length; j++) {n”, ” var data = cell.output_area.outputs[j];n”, ” if (data.data) {n”, ” // IPython >= 3 moved mimebundle to data attribute of outputn”, ” data = data.data;n”, ” }n”, ” if (data[‘text/html’] == html_output) {n”, ” return [cell, data, j];n”, ” }n”, ” }n”, ” }n”, ” }n”, “}n”, “n”, “// Register the function which deals with the matplotlib target/channel.n”, “// The kernel may be null if the page has been refreshed.n”, “if (IPython.notebook.kernel != null) {n”, ” IPython.notebook.kernel.comm_manager.register_target(‘matplotlib’, mpl.mpl_figure_comm);n”, “}n”

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” 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], “source”: [

“cutouts = processed.Spike_Explorer.get_spike_cutouts_at_sensor(611)n”, “plt.figure(figsize=(12,8))n”, “_ = plt.plot(np.transpose(cutouts*1e-3), color=’k’, alpha=0.2)n”, “plt.xlabel(‘Samples’)n”, “plt.ylabel(‘Voltage (µV)’)n”, “plt.title(‘Spike Cutout Overlay’)n”, “plt.show()”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

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

“### Network Explorer / STA Explorer<a id=’networkExplorer’></a>n”, “n”, “The Network_Explorer (this tool was called the STA_Explorer in earlier versions) contains datasets with spike-triggered averages. They represent the averaged activation on all sensors on the chip relative to the spiking activity of a single sensor/neuron and can be very useful to detect axonal propagation of spiking activity across the chip. n”, “n”, “The results of either the Spike_Explorer or the Spike_Sorter can serve as input to the Network_Explorer.n”, “n”, “The STA data is a block of data with dimensions samples x sensors_Y x sensors_X and can be visualized as a stack of 2-dimensional frames of a movie. “

]

}, {

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

{

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“/* Put everything inside the global mpl namespace /n”, “window.mpl = {};n”, “n”, “n”, “mpl.get_websocket_type = function() {n”, ” if (typeof(WebSocket) !== ‘undefined’) {n”, ” return WebSocket;n”, ” } else if (typeof(MozWebSocket) !== ‘undefined’) {n”, ” return MozWebSocket;n”, ” } else {n”, ” alert(‘Your browser does not have WebSocket support. ‘ +n”, ” ‘Please try Chrome, Safari or Firefox ≥ 6. ‘ +n”, ” ‘Firefox 4 and 5 are also supported but you ‘ +n”, ” ‘have to enable WebSockets in about:config.’);n”, ” };n”, “}n”, “n”, “mpl.figure = function(figure_id, websocket, ondownload, parent_element) {n”, ” this.id = figure_id;n”, “n”, ” this.ws = websocket;n”, “n”, ” this.supports_binary = (this.ws.binaryType != undefined);n”, “n”, ” if (!this.supports_binary) {n”, ” var warnings = document.getElementById(“mpl-warnings”);n”, ” if (warnings) {n”, ” warnings.style.display = ‘block’;n”, ” warnings.textContent = (n”, ” “This browser does not support binary websocket messages. ” +n”, ” “Performance may be slow.”);n”, ” }n”, ” }n”, “n”, ” this.imageObj = new Image();n”, “n”, ” this.context = undefined;n”, ” this.message = undefined;n”, ” this.canvas = undefined;n”, ” this.rubberband_canvas = undefined;n”, ” this.rubberband_context = undefined;n”, ” this.format_dropdown = undefined;n”, “n”, ” this.image_mode = ‘full’;n”, “n”, ” this.root = $(‘<div/>’);n”, ” this._root_extra_style(this.root)n”, ” this.root.attr(‘style’, ‘display: inline-block’);n”, “n”, ” $(parent_element).append(this.root);n”, “n”, ” this._init_header(this);n”, ” this._init_canvas(this);n”, ” this._init_toolbar(this);n”, “n”, ” var fig = this;n”, “n”, ” this.waiting = false;n”, “n”, ” this.ws.onopen = function () {n”, ” fig.send_message(“supports_binary”, {value: fig.supports_binary});n”, ” fig.send_message(“send_image_mode”, {});n”, ” if (mpl.ratio != 1) {n”, ” fig.send_message(“set_dpi_ratio”, {‘dpi_ratio’: mpl.ratio});n”, ” }n”, ” fig.send_message(“refresh”, {});n”, ” }n”, “n”, ” this.imageObj.onload = function() {n”, ” if (fig.image_mode == ‘full’) {n”, ” // Full images could contain transparency (where diff imagesn”, ” // almost always do), so we need to clear the canvas so thatn”, ” // there is no ghosting.n”, ” fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);n”, ” }n”, ” fig.context.drawImage(fig.imageObj, 0, 0);n”, ” };n”, “n”, ” this.imageObj.onunload = function() {n”, ” fig.ws.close();n”, ” }n”, “n”, ” this.ws.onmessage = this._make_on_message_function(this);n”, “n”, ” this.ondownload = ondownload;n”, “}n”, “n”, “mpl.figure.prototype._init_header = function() {n”, ” var titlebar = $(n”, ” ‘<div class=”ui-dialog-titlebar ui-widget-header ui-corner-all ‘ +n”, ” ‘ui-helper-clearfix”/>’);n”, ” var titletext = $(n”, ” ‘<div class=”ui-dialog-title” style=”width: 100%; ‘ +n”, ” ‘text-align: center; padding: 3px;”/>’);n”, ” titlebar.append(titletext)n”, ” this.root.append(titlebar);n”, ” this.header = titletext[0];n”, “}n”, “n”, “n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “n”, “mpl.figure.prototype._root_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “mpl.figure.prototype._init_canvas = function() {n”, ” var fig = this;n”, “n”, ” var canvas_div = $(‘<div/>’);n”, “n”, ” canvas_div.attr(‘style’, ‘position: relative; clear: both; outline: 0’);n”, “n”, ” function canvas_keyboard_event(event) {n”, ” return fig.key_event(event, event[‘data’]);n”, ” }n”, “n”, ” canvas_div.keydown(‘key_press’, canvas_keyboard_event);n”, ” canvas_div.keyup(‘key_release’, canvas_keyboard_event);n”, ” this.canvas_div = canvas_divn”, ” this._canvas_extra_style(canvas_div)n”, ” this.root.append(canvas_div);n”, “n”, ” var canvas = $(‘<canvas/>’);n”, ” canvas.addClass(‘mpl-canvas’);n”, ” canvas.attr(‘style’, “left: 0; top: 0; z-index: 0; outline: 0”)n”, “n”, ” this.canvas = canvas[0];n”, ” this.context = canvas[0].getContext(“2d”);n”, “n”, ” var backingStore = this.context.backingStorePixelRatio ||n”, “tthis.context.webkitBackingStorePixelRatio ||n”, “tthis.context.mozBackingStorePixelRatio ||n”, “tthis.context.msBackingStorePixelRatio ||n”, “tthis.context.oBackingStorePixelRatio ||n”, “tthis.context.backingStorePixelRatio || 1;n”, “n”, ” mpl.ratio = (window.devicePixelRatio || 1) / backingStore;n”, “n”, ” var rubberband = $(‘<canvas/>’);n”, ” rubberband.attr(‘style’, “position: absolute; left: 0; top: 0; z-index: 1;”)n”, “n”, ” var pass_mouse_events = true;n”, “n”, ” canvas_div.resizable({n”, ” start: function(event, ui) {n”, ” pass_mouse_events = false;n”, ” },n”, ” resize: function(event, ui) {n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” stop: function(event, ui) {n”, ” pass_mouse_events = true;n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” });n”, “n”, ” function mouse_event_fn(event) {n”, ” if (pass_mouse_events)n”, ” return fig.mouse_event(event, event[‘data’]);n”, ” }n”, “n”, ” rubberband.mousedown(‘button_press’, mouse_event_fn);n”, ” rubberband.mouseup(‘button_release’, mouse_event_fn);n”, ” // Throttle sequential mouse events to 1 every 20ms.n”, ” rubberband.mousemove(‘motion_notify’, mouse_event_fn);n”, “n”, ” rubberband.mouseenter(‘figure_enter’, mouse_event_fn);n”, ” rubberband.mouseleave(‘figure_leave’, mouse_event_fn);n”, “n”, ” canvas_div.on(“wheel”, function (event) {n”, ” event = event.originalEvent;n”, ” event[‘data’] = ‘scroll’n”, ” if (event.deltaY < 0) {n”, ” event.step = 1;n”, ” } else {n”, ” event.step = -1;n”, ” }n”, ” mouse_event_fn(event);n”, ” });n”, “n”, ” canvas_div.append(canvas);n”, ” canvas_div.append(rubberband);n”, “n”, ” this.rubberband = rubberband;n”, ” this.rubberband_canvas = rubberband[0];n”, ” this.rubberband_context = rubberband[0].getContext(“2d”);n”, ” this.rubberband_context.strokeStyle = “#000000”;n”, “n”, ” this._resize_canvas = function(width, height) {n”, ” // Keep the size of the canvas, canvas container, and rubber bandn”, ” // canvas in synch.n”, ” canvas_div.css(‘width’, width)n”, ” canvas_div.css(‘height’, height)n”, “n”, ” canvas.attr(‘width’, width * mpl.ratio);n”, ” canvas.attr(‘height’, height * mpl.ratio);n”, ” canvas.attr(‘style’, ‘width: ‘ + width + ‘px; height: ‘ + height + ‘px;’);n”, “n”, ” rubberband.attr(‘width’, width);n”, ” rubberband.attr(‘height’, height);n”, ” }n”, “n”, ” // Set the figure to an initial 600x600px, this will subsequently be updatedn”, ” // upon first draw.n”, ” this._resize_canvas(600, 600);n”, “n”, ” // Disable right mouse context menu.n”, ” $(this.rubberband_canvas).bind(“contextmenu”,function(e){n”, ” return false;n”, ” });n”, “n”, ” function set_focus () {n”, ” canvas.focus();n”, ” canvas_div.focus();n”, ” }n”, “n”, ” window.setTimeout(set_focus, 100);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items) {n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) {n”, ” // put a spacer in here.n”, ” continue;n”, ” }n”, ” var button = $(‘<button/>’);n”, ” button.addClass(‘ui-button ui-widget ui-state-default ui-corner-all ‘ +n”, ” ‘ui-button-icon-only’);n”, ” button.attr(‘role’, ‘button’);n”, ” button.attr(‘aria-disabled’, ‘false’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, “n”, ” var icon_img = $(‘<span/>’);n”, ” icon_img.addClass(‘ui-button-icon-primary ui-icon’);n”, ” icon_img.addClass(image);n”, ” icon_img.addClass(‘ui-corner-all’);n”, “n”, ” var tooltip_span = $(‘<span/>’);n”, ” tooltip_span.addClass(‘ui-button-text’);n”, ” tooltip_span.html(tooltip);n”, “n”, ” button.append(icon_img);n”, ” button.append(tooltip_span);n”, “n”, ” nav_element.append(button);n”, ” }n”, “n”, ” var fmt_picker_span = $(‘<span/>’);n”, “n”, ” var fmt_picker = $(‘<select/>’);n”, ” fmt_picker.addClass(‘mpl-toolbar-option ui-widget ui-widget-content’);n”, ” fmt_picker_span.append(fmt_picker);n”, ” nav_element.append(fmt_picker_span);n”, ” this.format_dropdown = fmt_picker[0];n”, “n”, ” for (var ind in mpl.extensions) {n”, ” var fmt = mpl.extensions[ind];n”, ” var option = $(n”, ” ‘<option/>’, {selected: fmt === mpl.default_extension}).html(fmt);n”, ” fmt_picker.append(option);n”, ” }n”, “n”, ” // Add hover states to the ui-buttonsn”, ” $( “.ui-button” ).hover(n”, ” function() { $(this).addClass(“ui-state-hover”);},n”, ” function() { $(this).removeClass(“ui-state-hover”);}n”, ” );n”, “n”, ” var status_bar = $(‘<span class=”mpl-message”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “}n”, “n”, “mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {n”, ” // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,n”, ” // which will in turn request a refresh of the image.n”, ” this.send_message(‘resize’, {‘width’: x_pixels, ‘height’: y_pixels});n”, “}n”, “n”, “mpl.figure.prototype.send_message = function(type, properties) {n”, ” properties[‘type’] = type;n”, ” properties[‘figure_id’] = this.id;n”, ” this.ws.send(JSON.stringify(properties));n”, “}n”, “n”, “mpl.figure.prototype.send_draw_message = function() {n”, ” if (!this.waiting) {n”, ” this.waiting = true;n”, ” this.ws.send(JSON.stringify({type: “draw”, figure_id: this.id}));n”, ” }n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” var format_dropdown = fig.format_dropdown;n”, ” var format = format_dropdown.options[format_dropdown.selectedIndex].value;n”, ” fig.ondownload(fig, format);n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_resize = function(fig, msg) {n”, ” var size = msg[‘size’];n”, ” if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {n”, ” fig._resize_canvas(size[0], size[1]);n”, ” fig.send_message(“refresh”, {});n”, ” };n”, “}n”, “n”, “mpl.figure.prototype.handle_rubberband = function(fig, msg) {n”, ” var x0 = msg[‘x0’] / mpl.ratio;n”, ” var y0 = (fig.canvas.height - msg[‘y0’]) / mpl.ratio;n”, ” var x1 = msg[‘x1’] / mpl.ratio;n”, ” var y1 = (fig.canvas.height - msg[‘y1’]) / mpl.ratio;n”, ” x0 = Math.floor(x0) + 0.5;n”, ” y0 = Math.floor(y0) + 0.5;n”, ” x1 = Math.floor(x1) + 0.5;n”, ” y1 = Math.floor(y1) + 0.5;n”, ” var min_x = Math.min(x0, x1);n”, ” var min_y = Math.min(y0, y1);n”, ” var width = Math.abs(x1 - x0);n”, ” var height = Math.abs(y1 - y0);n”, “n”, ” fig.rubberband_context.clearRect(n”, ” 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);n”, “n”, ” fig.rubberband_context.strokeRect(min_x, min_y, width, height);n”, “}n”, “n”, “mpl.figure.prototype.handle_figure_label = function(fig, msg) {n”, ” // Updates the figure title.n”, ” fig.header.textContent = msg[‘label’];n”, “}n”, “n”, “mpl.figure.prototype.handle_cursor = function(fig, msg) {n”, ” var cursor = msg[‘cursor’];n”, ” switch(cursor)n”, ” {n”, ” case 0:n”, ” cursor = ‘pointer’;n”, ” break;n”, ” case 1:n”, ” cursor = ‘default’;n”, ” break;n”, ” case 2:n”, ” cursor = ‘crosshair’;n”, ” break;n”, ” case 3:n”, ” cursor = ‘move’;n”, ” break;n”, ” }n”, ” fig.rubberband_canvas.style.cursor = cursor;n”, “}n”, “n”, “mpl.figure.prototype.handle_message = function(fig, msg) {n”, ” fig.message.textContent = msg[‘message’];n”, “}n”, “n”, “mpl.figure.prototype.handle_draw = function(fig, msg) {n”, ” // Request the server to send over a new figure.n”, ” fig.send_draw_message();n”, “}n”, “n”, “mpl.figure.prototype.handle_image_mode = function(fig, msg) {n”, ” fig.image_mode = msg[‘mode’];n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Called whenever the canvas gets updated.n”, ” this.send_message(“ack”, {});n”, “}n”, “n”, “// A function to construct a web socket function for onmessage handling.n”, “// Called in the figure constructor.n”, “mpl.figure.prototype._make_on_message_function = function(fig) {n”, ” return function socket_on_message(evt) {n”, ” if (evt.data instanceof Blob) {n”, ” / FIXME: We get “Resource interpreted as Image butn”, ” * transferred with MIME type text/plain:” errors onn”, ” * Chrome. But how to set the MIME type? It doesn’t seemn”, ” * to be part of the websocket stream /n”, ” evt.data.type = “image/png”;n”, “n”, ” / Free the memory for the previous frames /n”, ” if (fig.imageObj.src) {n”, ” (window.URL || window.webkitURL).revokeObjectURL(n”, ” fig.imageObj.src);n”, ” }n”, “n”, ” fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(n”, ” evt.data);n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, ” else if (typeof evt.data === ‘string’ && evt.data.slice(0, 21) == “data:image/png;base64”) {n”, ” fig.imageObj.src = evt.data;n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, “n”, ” var msg = JSON.parse(evt.data);n”, ” var msg_type = msg[‘type’];n”, “n”, ” // Call the “handle_{type}” callback, which takesn”, ” // the figure and JSON message as its only arguments.n”, ” try {n”, ” var callback = fig[“handle_” + msg_type];n”, ” } catch (e) {n”, ” console.log(“No handler for the ‘” + msg_type + “’ message type: “, msg);n”, ” return;n”, ” }n”, “n”, ” if (callback) {n”, ” try {n”, ” // console.log(“Handling ‘” + msg_type + “’ message: “, msg);n”, ” callback(fig, msg);n”, ” } catch (e) {n”, ” console.log(“Exception inside the ‘handler_” + msg_type + “’ callback:”, e, e.stack, msg);n”, ” }n”, ” }n”, ” };n”, “}n”, “n”, “// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvasn”, “mpl.findpos = function(e) {n”, ” //this section is from http://www.quirksmode.org/js/events_properties.htmln”, ” var targ;n”, ” if (!e)n”, ” e = window.event;n”, ” if (e.target)n”, ” targ = e.target;n”, ” else if (e.srcElement)n”, ” targ = e.srcElement;n”, ” if (targ.nodeType == 3) // defeat Safari bugn”, ” targ = targ.parentNode;n”, “n”, ” // jQuery normalizes the pageX and pageYn”, ” // pageX,Y are the mouse positions relative to the documentn”, ” // offset() returns the position of the element relative to the documentn”, ” var x = e.pageX - $(targ).offset().left;n”, ” var y = e.pageY - $(targ).offset().top;n”, “n”, ” return {“x”: x, “y”: y};n”, “};n”, “n”, “/n”, ” * return a copy of an object with only non-object keysn”, ” * we need this to avoid circular referencesn”, ” * http://stackoverflow.com/a/24161582/3208463n”, ” /n”, “function simpleKeys (original) {n”, ” return Object.keys(original).reduce(function (obj, key) {n”, ” if (typeof original[key] !== ‘object’)n”, ” obj[key] = original[key]n”, ” return obj;n”, ” }, {});n”, “}n”, “n”, “mpl.figure.prototype.mouse_event = function(event, name) {n”, ” var canvas_pos = mpl.findpos(event)n”, “n”, ” if (name === ‘button_press’)n”, ” {n”, ” this.canvas.focus();n”, ” this.canvas_div.focus();n”, ” }n”, “n”, ” var x = canvas_pos.x * mpl.ratio;n”, ” var y = canvas_pos.y * mpl.ratio;n”, “n”, ” this.send_message(name, {x: x, y: y, button: event.button,n”, ” step: event.step,n”, ” guiEvent: simpleKeys(event)});n”, “n”, ” / This prevents the web browser from automatically changing ton”, ” * the text insertion cursor when the button is pressed. We wantn”, ” * to control all of the cursor setting manually through then”, ” * ‘cursor’ event from matplotlib /n”, ” event.preventDefault();n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” // Handle any extra behaviour associated with a key eventn”, “}n”, “n”, “mpl.figure.prototype.key_event = function(event, name) {n”, “n”, ” // Prevent repeat eventsn”, ” if (name == ‘key_press’)n”, ” {n”, ” if (event.which === this._key)n”, ” return;n”, ” elsen”, ” this._key = event.which;n”, ” }n”, ” if (name == ‘key_release’)n”, ” this._key = null;n”, “n”, ” var value = ‘’;n”, ” if (event.ctrlKey && event.which != 17)n”, ” value += “ctrl+”;n”, ” if (event.altKey && event.which != 18)n”, ” value += “alt+”;n”, ” if (event.shiftKey && event.which != 16)n”, ” value += “shift+”;n”, “n”, ” value += ‘k’;n”, ” value += event.which.toString();n”, “n”, ” this._key_event_extra(event, name);n”, “n”, ” this.send_message(name, {key: value,n”, ” guiEvent: simpleKeys(event)});n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype.toolbar_button_onclick = function(name) {n”, ” if (name == ‘download’) {n”, ” this.handle_save(this, null);n”, ” } else {n”, ” this.send_message(“toolbar_button”, {name: name});n”, ” }n”, “};n”, “n”, “mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {n”, ” this.message.textContent = tooltip;n”, “};n”, “mpl.toolbar_items = [[“Home”, “Reset original view”, “fa fa-home icon-home”, “home”], [“Back”, “Back to previous view”, “fa fa-arrow-left icon-arrow-left”, “back”], [“Forward”, “Forward to next view”, “fa fa-arrow-right icon-arrow-right”, “forward”], [“”, “”, “”, “”], [“Pan”, “Pan axes with left mouse, zoom with right”, “fa fa-arrows icon-move”, “pan”], [“Zoom”, “Zoom to rectangle”, “fa fa-square-o icon-check-empty”, “zoom”], [“”, “”, “”, “”], [“Download”, “Download plot”, “fa fa-floppy-o icon-save”, “download”]];n”, “n”, “mpl.extensions = [“eps”, “jpeg”, “pdf”, “png”, “ps”, “raw”, “svg”, “tif”];n”, “n”, “mpl.default_extension = “png”;var comm_websocket_adapter = function(comm) {n”, ” // Create a “websocket”-like object which calls the given IPython commn”, ” // object with the appropriate methods. Currently this is a non binaryn”, ” // socket, so there is still some room for performance tuning.n”, ” var ws = {};n”, “n”, ” ws.close = function() {n”, ” comm.close()n”, ” };n”, ” ws.send = function(m) {n”, ” //console.log(‘sending’, m);n”, ” comm.send(m);n”, ” };n”, ” // Register the callback with on_msg.n”, ” comm.on_msg(function(msg) {n”, ” //console.log(‘receiving’, msg[‘content’][‘data’], msg);n”, ” // Pass the mpl event to the overridden (by mpl) onmessage function.n”, ” ws.onmessage(msg[‘content’][‘data’])n”, ” });n”, ” return ws;n”, “}n”, “n”, “mpl.mpl_figure_comm = function(comm, msg) {n”, ” // This is the function which gets called when the mpl processn”, ” // starts-up an IPython Comm through the “matplotlib” channel.n”, “n”, ” var id = msg.content.data.id;n”, ” // Get hold of the div created by the display call when the Commn”, ” // socket was opened in Python.n”, ” var element = $(“#” + id);n”, ” var ws_proxy = comm_websocket_adapter(comm)n”, “n”, ” function ondownload(figure, format) {n”, ” window.open(figure.imageObj.src);n”, ” }n”, “n”, ” var fig = new mpl.figure(id, ws_proxy,n”, ” ondownload,n”, ” element.get(0));n”, “n”, ” // Call onopen now - mpl needs it, as it is assuming we’ve passed it a realn”, ” // web socket which is closed, not our websocket->open comm proxy.n”, ” ws_proxy.onopen();n”, “n”, ” fig.parent_element = element.get(0);n”, ” fig.cell_info = mpl.find_output_cell(“<div id=’” + id + “’></div>”);n”, ” if (!fig.cell_info) {n”, ” console.error(“Failed to find cell for figure”, id, fig);n”, ” return;n”, ” }n”, “n”, ” var output_index = fig.cell_info[2]n”, ” var cell = fig.cell_info[0];n”, “n”, “};n”, “n”, “mpl.figure.prototype.handle_close = function(fig, msg) {n”, ” var width = fig.canvas.width/mpl.ration”, ” fig.root.unbind(‘remove’)n”, “n”, ” // Update the output cell to use the data from the current canvas.n”, ” fig.push_to_output();n”, ” var dataURL = fig.canvas.toDataURL();n”, ” // Re-enable the keyboard manager in IPython - without this line, in FF,n”, ” // the notebook keyboard shortcuts fail.n”, ” IPython.keyboard_manager.enable()n”, ” $(fig.parent_element).html(‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’);n”, ” fig.close_ws(fig, msg);n”, “}n”, “n”, “mpl.figure.prototype.close_ws = function(fig, msg){n”, ” fig.send_message(‘closing’, msg);n”, ” // fig.ws.close()n”, “}n”, “n”, “mpl.figure.prototype.push_to_output = function(remove_interactive) {n”, ” // Turn the data on the canvas into data in the output cell.n”, ” var width = this.canvas.width/mpl.ration”, ” var dataURL = this.canvas.toDataURL();n”, ” this.cell_info[1][‘text/html’] = ‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’;n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Tell IPython that the notebook contents must change.n”, ” IPython.notebook.set_dirty(true);n”, ” this.send_message(“ack”, {});n”, ” var fig = this;n”, ” // Wait a second, then push the new image to the DOM son”, ” // that it is saved nicely (might be nice to debounce this).n”, ” setTimeout(function () { fig.push_to_output() }, 1000);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items){n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) { continue; };n”, “n”, ” var button = $(‘<button class=”btn btn-default” href=”#” title=”’ + name + ‘”><i class=”fa ‘ + image + ‘ fa-lg”></i></button>’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, ” nav_element.append(button);n”, ” }n”, “n”, ” // Add the status bar.n”, ” var status_bar = $(‘<span class=”mpl-message” style=”text-align:right; float: right;”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “n”, ” // Add the close button to the window.n”, ” var buttongrp = $(‘<div class=”btn-group inline pull-right”></div>’);n”, ” var button = $(‘<button class=”btn btn-mini btn-primary” href=”#” title=”Stop Interaction”><i class=”fa fa-power-off icon-remove icon-large”></i></button>’);n”, ” button.click(function (evt) { fig.handle_close(fig, {}); } );n”, ” button.mouseover(‘Stop Interaction’, toolbar_mouse_event);n”, ” buttongrp.append(button);n”, ” var titlebar = this.root.find($(‘.ui-dialog-titlebar’));n”, ” titlebar.prepend(buttongrp);n”, “}n”, “n”, “mpl.figure.prototype._root_extra_style = function(el){n”, ” var fig = thisn”, ” el.on(“remove”, function(){n”, “tfig.close_ws(fig, {});n”, ” });n”, “}n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(el){n”, ” // this is important to make the div ‘focusablen”, ” el.attr(‘tabindex’, 0)n”, ” // reach out to IPython and tell the keyboard manager to turn it’s selfn”, ” // off when our div gets focusn”, “n”, ” // location in version 3n”, ” if (IPython.notebook.keyboard_manager) {n”, ” IPython.notebook.keyboard_manager.register_events(el);n”, ” }n”, ” else {n”, ” // location in version 2n”, ” IPython.keyboard_manager.register_events(el);n”, ” }n”, “n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” var manager = IPython.notebook.keyboard_manager;n”, ” if (!manager)n”, ” manager = IPython.keyboard_manager;n”, “n”, ” // Check for shift+entern”, ” if (event.shiftKey && event.which == 13) {n”, ” this.canvas_div.blur();n”, ” event.shiftKey = false;n”, ” // Send a “J” for go to next celln”, ” event.which = 74;n”, ” event.keyCode = 74;n”, ” manager.command_mode();n”, ” manager.handle_keydown(event);n”, ” }n”, “}n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” fig.ondownload(fig, null);n”, “}n”, “n”, “n”, “mpl.find_output_cell = function(html_output) {n”, ” // Return the cell and output element which can be found *uniquely in the notebook.n”, ” // Note - this is a bit hacky, but it is done because the “notebook_saving.Notebook”n”, ” // IPython event is triggered only after the cells have been serialised, which forn”, ” // our purposes (turning an active figure into a static one), is too late.n”, ” var cells = IPython.notebook.get_cells();n”, ” var ncells = cells.length;n”, ” for (var i=0; i<ncells; i++) {n”, ” var cell = cells[i];n”, ” if (cell.cell_type === ‘code’){n”, ” for (var j=0; j<cell.output_area.outputs.length; j++) {n”, ” var data = cell.output_area.outputs[j];n”, ” if (data.data) {n”, ” // IPython >= 3 moved mimebundle to data attribute of outputn”, ” data = data.data;n”, ” }n”, ” if (data[‘text/html’] == html_output) {n”, ” return [cell, data, j];n”, ” }n”, ” }n”, ” }n”, ” }n”, “}n”, “n”, “// Register the function which deals with the matplotlib target/channel.n”, “// The kernel may be null if the page has been refreshed.n”, “if (IPython.notebook.kernel != null) {n”, ” IPython.notebook.kernel.comm_manager.register_target(‘matplotlib’, mpl.mpl_figure_comm);n”, “}n”

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

}, {

“data”: {

“application/vnd.jupyter.widget-view+json”: {

“model_id”: “38439d1047814efab300a1a31c8ec201”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“interactive(children=(IntSlider(value=0, description=’Frame’, max=79), Output()), _dom_classes=(‘widget-intera…”

]

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

}

], “source”: [

“%matplotlib notebookn”, “n”, “sta = processed.Network_Explorer.get_sta_entity_by_sourceID(14)n”, “n”, “images = sta.datan”, “num_of_images = images.shape[0]-1n”, “n”, “fig = plt.figure(figsize=(6,6))n”, “ax = fig.add_subplot(1,1,1)n”, “plt.title(‘STA Unit 14’)n”, “plt.box(False)n”, “fig.show()n”, “n”, “def updateFrame(Frame):n”, ” ax.imshow(np.transpose(images[Frame,::]), cmap=”gray”, vmin=np.amin(images), vmax=np.amax(images))n”, ” n”, “_ = interact(updateFrame, Frame=widgets.IntSlider(min=0,max=num_of_images,step=1,value=0))”

]

}, {

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

“Network_Explorer data sources in files created by CMOS-MEA-Tools Version 2.4 and newer may contain axon tracking results in the processed.Network_Explorer.NeuralNetwork property. The extracted axon paths for individual units can be visualized like this:”

]

}, {

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

{

“data”: {

“application/javascript”: [

“/* Put everything inside the global mpl namespace /n”, “window.mpl = {};n”, “n”, “n”, “mpl.get_websocket_type = function() {n”, ” if (typeof(WebSocket) !== ‘undefined’) {n”, ” return WebSocket;n”, ” } else if (typeof(MozWebSocket) !== ‘undefined’) {n”, ” return MozWebSocket;n”, ” } else {n”, ” alert(‘Your browser does not have WebSocket support. ‘ +n”, ” ‘Please try Chrome, Safari or Firefox ≥ 6. ‘ +n”, ” ‘Firefox 4 and 5 are also supported but you ‘ +n”, ” ‘have to enable WebSockets in about:config.’);n”, ” };n”, “}n”, “n”, “mpl.figure = function(figure_id, websocket, ondownload, parent_element) {n”, ” this.id = figure_id;n”, “n”, ” this.ws = websocket;n”, “n”, ” this.supports_binary = (this.ws.binaryType != undefined);n”, “n”, ” if (!this.supports_binary) {n”, ” var warnings = document.getElementById(“mpl-warnings”);n”, ” if (warnings) {n”, ” warnings.style.display = ‘block’;n”, ” warnings.textContent = (n”, ” “This browser does not support binary websocket messages. ” +n”, ” “Performance may be slow.”);n”, ” }n”, ” }n”, “n”, ” this.imageObj = new Image();n”, “n”, ” this.context = undefined;n”, ” this.message = undefined;n”, ” this.canvas = undefined;n”, ” this.rubberband_canvas = undefined;n”, ” this.rubberband_context = undefined;n”, ” this.format_dropdown = undefined;n”, “n”, ” this.image_mode = ‘full’;n”, “n”, ” this.root = $(‘<div/>’);n”, ” this._root_extra_style(this.root)n”, ” this.root.attr(‘style’, ‘display: inline-block’);n”, “n”, ” $(parent_element).append(this.root);n”, “n”, ” this._init_header(this);n”, ” this._init_canvas(this);n”, ” this._init_toolbar(this);n”, “n”, ” var fig = this;n”, “n”, ” this.waiting = false;n”, “n”, ” this.ws.onopen = function () {n”, ” fig.send_message(“supports_binary”, {value: fig.supports_binary});n”, ” fig.send_message(“send_image_mode”, {});n”, ” if (mpl.ratio != 1) {n”, ” fig.send_message(“set_dpi_ratio”, {‘dpi_ratio’: mpl.ratio});n”, ” }n”, ” fig.send_message(“refresh”, {});n”, ” }n”, “n”, ” this.imageObj.onload = function() {n”, ” if (fig.image_mode == ‘full’) {n”, ” // Full images could contain transparency (where diff imagesn”, ” // almost always do), so we need to clear the canvas so thatn”, ” // there is no ghosting.n”, ” fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);n”, ” }n”, ” fig.context.drawImage(fig.imageObj, 0, 0);n”, ” };n”, “n”, ” this.imageObj.onunload = function() {n”, ” fig.ws.close();n”, ” }n”, “n”, ” this.ws.onmessage = this._make_on_message_function(this);n”, “n”, ” this.ondownload = ondownload;n”, “}n”, “n”, “mpl.figure.prototype._init_header = function() {n”, ” var titlebar = $(n”, ” ‘<div class=”ui-dialog-titlebar ui-widget-header ui-corner-all ‘ +n”, ” ‘ui-helper-clearfix”/>’);n”, ” var titletext = $(n”, ” ‘<div class=”ui-dialog-title” style=”width: 100%; ‘ +n”, ” ‘text-align: center; padding: 3px;”/>’);n”, ” titlebar.append(titletext)n”, ” this.root.append(titlebar);n”, ” this.header = titletext[0];n”, “}n”, “n”, “n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “n”, “mpl.figure.prototype._root_extra_style = function(canvas_div) {n”, “n”, “}n”, “n”, “mpl.figure.prototype._init_canvas = function() {n”, ” var fig = this;n”, “n”, ” var canvas_div = $(‘<div/>’);n”, “n”, ” canvas_div.attr(‘style’, ‘position: relative; clear: both; outline: 0’);n”, “n”, ” function canvas_keyboard_event(event) {n”, ” return fig.key_event(event, event[‘data’]);n”, ” }n”, “n”, ” canvas_div.keydown(‘key_press’, canvas_keyboard_event);n”, ” canvas_div.keyup(‘key_release’, canvas_keyboard_event);n”, ” this.canvas_div = canvas_divn”, ” this._canvas_extra_style(canvas_div)n”, ” this.root.append(canvas_div);n”, “n”, ” var canvas = $(‘<canvas/>’);n”, ” canvas.addClass(‘mpl-canvas’);n”, ” canvas.attr(‘style’, “left: 0; top: 0; z-index: 0; outline: 0”)n”, “n”, ” this.canvas = canvas[0];n”, ” this.context = canvas[0].getContext(“2d”);n”, “n”, ” var backingStore = this.context.backingStorePixelRatio ||n”, “tthis.context.webkitBackingStorePixelRatio ||n”, “tthis.context.mozBackingStorePixelRatio ||n”, “tthis.context.msBackingStorePixelRatio ||n”, “tthis.context.oBackingStorePixelRatio ||n”, “tthis.context.backingStorePixelRatio || 1;n”, “n”, ” mpl.ratio = (window.devicePixelRatio || 1) / backingStore;n”, “n”, ” var rubberband = $(‘<canvas/>’);n”, ” rubberband.attr(‘style’, “position: absolute; left: 0; top: 0; z-index: 1;”)n”, “n”, ” var pass_mouse_events = true;n”, “n”, ” canvas_div.resizable({n”, ” start: function(event, ui) {n”, ” pass_mouse_events = false;n”, ” },n”, ” resize: function(event, ui) {n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” stop: function(event, ui) {n”, ” pass_mouse_events = true;n”, ” fig.request_resize(ui.size.width, ui.size.height);n”, ” },n”, ” });n”, “n”, ” function mouse_event_fn(event) {n”, ” if (pass_mouse_events)n”, ” return fig.mouse_event(event, event[‘data’]);n”, ” }n”, “n”, ” rubberband.mousedown(‘button_press’, mouse_event_fn);n”, ” rubberband.mouseup(‘button_release’, mouse_event_fn);n”, ” // Throttle sequential mouse events to 1 every 20ms.n”, ” rubberband.mousemove(‘motion_notify’, mouse_event_fn);n”, “n”, ” rubberband.mouseenter(‘figure_enter’, mouse_event_fn);n”, ” rubberband.mouseleave(‘figure_leave’, mouse_event_fn);n”, “n”, ” canvas_div.on(“wheel”, function (event) {n”, ” event = event.originalEvent;n”, ” event[‘data’] = ‘scroll’n”, ” if (event.deltaY < 0) {n”, ” event.step = 1;n”, ” } else {n”, ” event.step = -1;n”, ” }n”, ” mouse_event_fn(event);n”, ” });n”, “n”, ” canvas_div.append(canvas);n”, ” canvas_div.append(rubberband);n”, “n”, ” this.rubberband = rubberband;n”, ” this.rubberband_canvas = rubberband[0];n”, ” this.rubberband_context = rubberband[0].getContext(“2d”);n”, ” this.rubberband_context.strokeStyle = “#000000”;n”, “n”, ” this._resize_canvas = function(width, height) {n”, ” // Keep the size of the canvas, canvas container, and rubber bandn”, ” // canvas in synch.n”, ” canvas_div.css(‘width’, width)n”, ” canvas_div.css(‘height’, height)n”, “n”, ” canvas.attr(‘width’, width * mpl.ratio);n”, ” canvas.attr(‘height’, height * mpl.ratio);n”, ” canvas.attr(‘style’, ‘width: ‘ + width + ‘px; height: ‘ + height + ‘px;’);n”, “n”, ” rubberband.attr(‘width’, width);n”, ” rubberband.attr(‘height’, height);n”, ” }n”, “n”, ” // Set the figure to an initial 600x600px, this will subsequently be updatedn”, ” // upon first draw.n”, ” this._resize_canvas(600, 600);n”, “n”, ” // Disable right mouse context menu.n”, ” $(this.rubberband_canvas).bind(“contextmenu”,function(e){n”, ” return false;n”, ” });n”, “n”, ” function set_focus () {n”, ” canvas.focus();n”, ” canvas_div.focus();n”, ” }n”, “n”, ” window.setTimeout(set_focus, 100);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items) {n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) {n”, ” // put a spacer in here.n”, ” continue;n”, ” }n”, ” var button = $(‘<button/>’);n”, ” button.addClass(‘ui-button ui-widget ui-state-default ui-corner-all ‘ +n”, ” ‘ui-button-icon-only’);n”, ” button.attr(‘role’, ‘button’);n”, ” button.attr(‘aria-disabled’, ‘false’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, “n”, ” var icon_img = $(‘<span/>’);n”, ” icon_img.addClass(‘ui-button-icon-primary ui-icon’);n”, ” icon_img.addClass(image);n”, ” icon_img.addClass(‘ui-corner-all’);n”, “n”, ” var tooltip_span = $(‘<span/>’);n”, ” tooltip_span.addClass(‘ui-button-text’);n”, ” tooltip_span.html(tooltip);n”, “n”, ” button.append(icon_img);n”, ” button.append(tooltip_span);n”, “n”, ” nav_element.append(button);n”, ” }n”, “n”, ” var fmt_picker_span = $(‘<span/>’);n”, “n”, ” var fmt_picker = $(‘<select/>’);n”, ” fmt_picker.addClass(‘mpl-toolbar-option ui-widget ui-widget-content’);n”, ” fmt_picker_span.append(fmt_picker);n”, ” nav_element.append(fmt_picker_span);n”, ” this.format_dropdown = fmt_picker[0];n”, “n”, ” for (var ind in mpl.extensions) {n”, ” var fmt = mpl.extensions[ind];n”, ” var option = $(n”, ” ‘<option/>’, {selected: fmt === mpl.default_extension}).html(fmt);n”, ” fmt_picker.append(option);n”, ” }n”, “n”, ” // Add hover states to the ui-buttonsn”, ” $( “.ui-button” ).hover(n”, ” function() { $(this).addClass(“ui-state-hover”);},n”, ” function() { $(this).removeClass(“ui-state-hover”);}n”, ” );n”, “n”, ” var status_bar = $(‘<span class=”mpl-message”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “}n”, “n”, “mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {n”, ” // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,n”, ” // which will in turn request a refresh of the image.n”, ” this.send_message(‘resize’, {‘width’: x_pixels, ‘height’: y_pixels});n”, “}n”, “n”, “mpl.figure.prototype.send_message = function(type, properties) {n”, ” properties[‘type’] = type;n”, ” properties[‘figure_id’] = this.id;n”, ” this.ws.send(JSON.stringify(properties));n”, “}n”, “n”, “mpl.figure.prototype.send_draw_message = function() {n”, ” if (!this.waiting) {n”, ” this.waiting = true;n”, ” this.ws.send(JSON.stringify({type: “draw”, figure_id: this.id}));n”, ” }n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” var format_dropdown = fig.format_dropdown;n”, ” var format = format_dropdown.options[format_dropdown.selectedIndex].value;n”, ” fig.ondownload(fig, format);n”, “}n”, “n”, “n”, “mpl.figure.prototype.handle_resize = function(fig, msg) {n”, ” var size = msg[‘size’];n”, ” if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {n”, ” fig._resize_canvas(size[0], size[1]);n”, ” fig.send_message(“refresh”, {});n”, ” };n”, “}n”, “n”, “mpl.figure.prototype.handle_rubberband = function(fig, msg) {n”, ” var x0 = msg[‘x0’] / mpl.ratio;n”, ” var y0 = (fig.canvas.height - msg[‘y0’]) / mpl.ratio;n”, ” var x1 = msg[‘x1’] / mpl.ratio;n”, ” var y1 = (fig.canvas.height - msg[‘y1’]) / mpl.ratio;n”, ” x0 = Math.floor(x0) + 0.5;n”, ” y0 = Math.floor(y0) + 0.5;n”, ” x1 = Math.floor(x1) + 0.5;n”, ” y1 = Math.floor(y1) + 0.5;n”, ” var min_x = Math.min(x0, x1);n”, ” var min_y = Math.min(y0, y1);n”, ” var width = Math.abs(x1 - x0);n”, ” var height = Math.abs(y1 - y0);n”, “n”, ” fig.rubberband_context.clearRect(n”, ” 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);n”, “n”, ” fig.rubberband_context.strokeRect(min_x, min_y, width, height);n”, “}n”, “n”, “mpl.figure.prototype.handle_figure_label = function(fig, msg) {n”, ” // Updates the figure title.n”, ” fig.header.textContent = msg[‘label’];n”, “}n”, “n”, “mpl.figure.prototype.handle_cursor = function(fig, msg) {n”, ” var cursor = msg[‘cursor’];n”, ” switch(cursor)n”, ” {n”, ” case 0:n”, ” cursor = ‘pointer’;n”, ” break;n”, ” case 1:n”, ” cursor = ‘default’;n”, ” break;n”, ” case 2:n”, ” cursor = ‘crosshair’;n”, ” break;n”, ” case 3:n”, ” cursor = ‘move’;n”, ” break;n”, ” }n”, ” fig.rubberband_canvas.style.cursor = cursor;n”, “}n”, “n”, “mpl.figure.prototype.handle_message = function(fig, msg) {n”, ” fig.message.textContent = msg[‘message’];n”, “}n”, “n”, “mpl.figure.prototype.handle_draw = function(fig, msg) {n”, ” // Request the server to send over a new figure.n”, ” fig.send_draw_message();n”, “}n”, “n”, “mpl.figure.prototype.handle_image_mode = function(fig, msg) {n”, ” fig.image_mode = msg[‘mode’];n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Called whenever the canvas gets updated.n”, ” this.send_message(“ack”, {});n”, “}n”, “n”, “// A function to construct a web socket function for onmessage handling.n”, “// Called in the figure constructor.n”, “mpl.figure.prototype._make_on_message_function = function(fig) {n”, ” return function socket_on_message(evt) {n”, ” if (evt.data instanceof Blob) {n”, ” / FIXME: We get “Resource interpreted as Image butn”, ” * transferred with MIME type text/plain:” errors onn”, ” * Chrome. But how to set the MIME type? It doesn’t seemn”, ” * to be part of the websocket stream /n”, ” evt.data.type = “image/png”;n”, “n”, ” / Free the memory for the previous frames /n”, ” if (fig.imageObj.src) {n”, ” (window.URL || window.webkitURL).revokeObjectURL(n”, ” fig.imageObj.src);n”, ” }n”, “n”, ” fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(n”, ” evt.data);n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, ” else if (typeof evt.data === ‘string’ && evt.data.slice(0, 21) == “data:image/png;base64”) {n”, ” fig.imageObj.src = evt.data;n”, ” fig.updated_canvas_event();n”, ” fig.waiting = false;n”, ” return;n”, ” }n”, “n”, ” var msg = JSON.parse(evt.data);n”, ” var msg_type = msg[‘type’];n”, “n”, ” // Call the “handle_{type}” callback, which takesn”, ” // the figure and JSON message as its only arguments.n”, ” try {n”, ” var callback = fig[“handle_” + msg_type];n”, ” } catch (e) {n”, ” console.log(“No handler for the ‘” + msg_type + “’ message type: “, msg);n”, ” return;n”, ” }n”, “n”, ” if (callback) {n”, ” try {n”, ” // console.log(“Handling ‘” + msg_type + “’ message: “, msg);n”, ” callback(fig, msg);n”, ” } catch (e) {n”, ” console.log(“Exception inside the ‘handler_” + msg_type + “’ callback:”, e, e.stack, msg);n”, ” }n”, ” }n”, ” };n”, “}n”, “n”, “// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvasn”, “mpl.findpos = function(e) {n”, ” //this section is from http://www.quirksmode.org/js/events_properties.htmln”, ” var targ;n”, ” if (!e)n”, ” e = window.event;n”, ” if (e.target)n”, ” targ = e.target;n”, ” else if (e.srcElement)n”, ” targ = e.srcElement;n”, ” if (targ.nodeType == 3) // defeat Safari bugn”, ” targ = targ.parentNode;n”, “n”, ” // jQuery normalizes the pageX and pageYn”, ” // pageX,Y are the mouse positions relative to the documentn”, ” // offset() returns the position of the element relative to the documentn”, ” var x = e.pageX - $(targ).offset().left;n”, ” var y = e.pageY - $(targ).offset().top;n”, “n”, ” return {“x”: x, “y”: y};n”, “};n”, “n”, “/n”, ” * return a copy of an object with only non-object keysn”, ” * we need this to avoid circular referencesn”, ” * http://stackoverflow.com/a/24161582/3208463n”, ” /n”, “function simpleKeys (original) {n”, ” return Object.keys(original).reduce(function (obj, key) {n”, ” if (typeof original[key] !== ‘object’)n”, ” obj[key] = original[key]n”, ” return obj;n”, ” }, {});n”, “}n”, “n”, “mpl.figure.prototype.mouse_event = function(event, name) {n”, ” var canvas_pos = mpl.findpos(event)n”, “n”, ” if (name === ‘button_press’)n”, ” {n”, ” this.canvas.focus();n”, ” this.canvas_div.focus();n”, ” }n”, “n”, ” var x = canvas_pos.x * mpl.ratio;n”, ” var y = canvas_pos.y * mpl.ratio;n”, “n”, ” this.send_message(name, {x: x, y: y, button: event.button,n”, ” step: event.step,n”, ” guiEvent: simpleKeys(event)});n”, “n”, ” / This prevents the web browser from automatically changing ton”, ” * the text insertion cursor when the button is pressed. We wantn”, ” * to control all of the cursor setting manually through then”, ” * ‘cursor’ event from matplotlib /n”, ” event.preventDefault();n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” // Handle any extra behaviour associated with a key eventn”, “}n”, “n”, “mpl.figure.prototype.key_event = function(event, name) {n”, “n”, ” // Prevent repeat eventsn”, ” if (name == ‘key_press’)n”, ” {n”, ” if (event.which === this._key)n”, ” return;n”, ” elsen”, ” this._key = event.which;n”, ” }n”, ” if (name == ‘key_release’)n”, ” this._key = null;n”, “n”, ” var value = ‘’;n”, ” if (event.ctrlKey && event.which != 17)n”, ” value += “ctrl+”;n”, ” if (event.altKey && event.which != 18)n”, ” value += “alt+”;n”, ” if (event.shiftKey && event.which != 16)n”, ” value += “shift+”;n”, “n”, ” value += ‘k’;n”, ” value += event.which.toString();n”, “n”, ” this._key_event_extra(event, name);n”, “n”, ” this.send_message(name, {key: value,n”, ” guiEvent: simpleKeys(event)});n”, ” return false;n”, “}n”, “n”, “mpl.figure.prototype.toolbar_button_onclick = function(name) {n”, ” if (name == ‘download’) {n”, ” this.handle_save(this, null);n”, ” } else {n”, ” this.send_message(“toolbar_button”, {name: name});n”, ” }n”, “};n”, “n”, “mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {n”, ” this.message.textContent = tooltip;n”, “};n”, “mpl.toolbar_items = [[“Home”, “Reset original view”, “fa fa-home icon-home”, “home”], [“Back”, “Back to previous view”, “fa fa-arrow-left icon-arrow-left”, “back”], [“Forward”, “Forward to next view”, “fa fa-arrow-right icon-arrow-right”, “forward”], [“”, “”, “”, “”], [“Pan”, “Pan axes with left mouse, zoom with right”, “fa fa-arrows icon-move”, “pan”], [“Zoom”, “Zoom to rectangle”, “fa fa-square-o icon-check-empty”, “zoom”], [“”, “”, “”, “”], [“Download”, “Download plot”, “fa fa-floppy-o icon-save”, “download”]];n”, “n”, “mpl.extensions = [“eps”, “jpeg”, “pdf”, “png”, “ps”, “raw”, “svg”, “tif”];n”, “n”, “mpl.default_extension = “png”;var comm_websocket_adapter = function(comm) {n”, ” // Create a “websocket”-like object which calls the given IPython commn”, ” // object with the appropriate methods. Currently this is a non binaryn”, ” // socket, so there is still some room for performance tuning.n”, ” var ws = {};n”, “n”, ” ws.close = function() {n”, ” comm.close()n”, ” };n”, ” ws.send = function(m) {n”, ” //console.log(‘sending’, m);n”, ” comm.send(m);n”, ” };n”, ” // Register the callback with on_msg.n”, ” comm.on_msg(function(msg) {n”, ” //console.log(‘receiving’, msg[‘content’][‘data’], msg);n”, ” // Pass the mpl event to the overridden (by mpl) onmessage function.n”, ” ws.onmessage(msg[‘content’][‘data’])n”, ” });n”, ” return ws;n”, “}n”, “n”, “mpl.mpl_figure_comm = function(comm, msg) {n”, ” // This is the function which gets called when the mpl processn”, ” // starts-up an IPython Comm through the “matplotlib” channel.n”, “n”, ” var id = msg.content.data.id;n”, ” // Get hold of the div created by the display call when the Commn”, ” // socket was opened in Python.n”, ” var element = $(“#” + id);n”, ” var ws_proxy = comm_websocket_adapter(comm)n”, “n”, ” function ondownload(figure, format) {n”, ” window.open(figure.imageObj.src);n”, ” }n”, “n”, ” var fig = new mpl.figure(id, ws_proxy,n”, ” ondownload,n”, ” element.get(0));n”, “n”, ” // Call onopen now - mpl needs it, as it is assuming we’ve passed it a realn”, ” // web socket which is closed, not our websocket->open comm proxy.n”, ” ws_proxy.onopen();n”, “n”, ” fig.parent_element = element.get(0);n”, ” fig.cell_info = mpl.find_output_cell(“<div id=’” + id + “’></div>”);n”, ” if (!fig.cell_info) {n”, ” console.error(“Failed to find cell for figure”, id, fig);n”, ” return;n”, ” }n”, “n”, ” var output_index = fig.cell_info[2]n”, ” var cell = fig.cell_info[0];n”, “n”, “};n”, “n”, “mpl.figure.prototype.handle_close = function(fig, msg) {n”, ” var width = fig.canvas.width/mpl.ration”, ” fig.root.unbind(‘remove’)n”, “n”, ” // Update the output cell to use the data from the current canvas.n”, ” fig.push_to_output();n”, ” var dataURL = fig.canvas.toDataURL();n”, ” // Re-enable the keyboard manager in IPython - without this line, in FF,n”, ” // the notebook keyboard shortcuts fail.n”, ” IPython.keyboard_manager.enable()n”, ” $(fig.parent_element).html(‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’);n”, ” fig.close_ws(fig, msg);n”, “}n”, “n”, “mpl.figure.prototype.close_ws = function(fig, msg){n”, ” fig.send_message(‘closing’, msg);n”, ” // fig.ws.close()n”, “}n”, “n”, “mpl.figure.prototype.push_to_output = function(remove_interactive) {n”, ” // Turn the data on the canvas into data in the output cell.n”, ” var width = this.canvas.width/mpl.ration”, ” var dataURL = this.canvas.toDataURL();n”, ” this.cell_info[1][‘text/html’] = ‘<img src=”’ + dataURL + ‘” width=”’ + width + ‘”>’;n”, “}n”, “n”, “mpl.figure.prototype.updated_canvas_event = function() {n”, ” // Tell IPython that the notebook contents must change.n”, ” IPython.notebook.set_dirty(true);n”, ” this.send_message(“ack”, {});n”, ” var fig = this;n”, ” // Wait a second, then push the new image to the DOM son”, ” // that it is saved nicely (might be nice to debounce this).n”, ” setTimeout(function () { fig.push_to_output() }, 1000);n”, “}n”, “n”, “mpl.figure.prototype._init_toolbar = function() {n”, ” var fig = this;n”, “n”, ” var nav_element = $(‘<div/>’);n”, ” nav_element.attr(‘style’, ‘width: 100%’);n”, ” this.root.append(nav_element);n”, “n”, ” // Define a callback function for later on.n”, ” function toolbar_event(event) {n”, ” return fig.toolbar_button_onclick(event[‘data’]);n”, ” }n”, ” function toolbar_mouse_event(event) {n”, ” return fig.toolbar_button_onmouseover(event[‘data’]);n”, ” }n”, “n”, ” for(var toolbar_ind in mpl.toolbar_items){n”, ” var name = mpl.toolbar_items[toolbar_ind][0];n”, ” var tooltip = mpl.toolbar_items[toolbar_ind][1];n”, ” var image = mpl.toolbar_items[toolbar_ind][2];n”, ” var method_name = mpl.toolbar_items[toolbar_ind][3];n”, “n”, ” if (!name) { continue; };n”, “n”, ” var button = $(‘<button class=”btn btn-default” href=”#” title=”’ + name + ‘”><i class=”fa ‘ + image + ‘ fa-lg”></i></button>’);n”, ” button.click(method_name, toolbar_event);n”, ” button.mouseover(tooltip, toolbar_mouse_event);n”, ” nav_element.append(button);n”, ” }n”, “n”, ” // Add the status bar.n”, ” var status_bar = $(‘<span class=”mpl-message” style=”text-align:right; float: right;”/>’);n”, ” nav_element.append(status_bar);n”, ” this.message = status_bar[0];n”, “n”, ” // Add the close button to the window.n”, ” var buttongrp = $(‘<div class=”btn-group inline pull-right”></div>’);n”, ” var button = $(‘<button class=”btn btn-mini btn-primary” href=”#” title=”Stop Interaction”><i class=”fa fa-power-off icon-remove icon-large”></i></button>’);n”, ” button.click(function (evt) { fig.handle_close(fig, {}); } );n”, ” button.mouseover(‘Stop Interaction’, toolbar_mouse_event);n”, ” buttongrp.append(button);n”, ” var titlebar = this.root.find($(‘.ui-dialog-titlebar’));n”, ” titlebar.prepend(buttongrp);n”, “}n”, “n”, “mpl.figure.prototype._root_extra_style = function(el){n”, ” var fig = thisn”, ” el.on(“remove”, function(){n”, “tfig.close_ws(fig, {});n”, ” });n”, “}n”, “n”, “mpl.figure.prototype._canvas_extra_style = function(el){n”, ” // this is important to make the div ‘focusablen”, ” el.attr(‘tabindex’, 0)n”, ” // reach out to IPython and tell the keyboard manager to turn it’s selfn”, ” // off when our div gets focusn”, “n”, ” // location in version 3n”, ” if (IPython.notebook.keyboard_manager) {n”, ” IPython.notebook.keyboard_manager.register_events(el);n”, ” }n”, ” else {n”, ” // location in version 2n”, ” IPython.keyboard_manager.register_events(el);n”, ” }n”, “n”, “}n”, “n”, “mpl.figure.prototype._key_event_extra = function(event, name) {n”, ” var manager = IPython.notebook.keyboard_manager;n”, ” if (!manager)n”, ” manager = IPython.keyboard_manager;n”, “n”, ” // Check for shift+entern”, ” if (event.shiftKey && event.which == 13) {n”, ” this.canvas_div.blur();n”, ” event.shiftKey = false;n”, ” // Send a “J” for go to next celln”, ” event.which = 74;n”, ” event.keyCode = 74;n”, ” manager.command_mode();n”, ” manager.handle_keydown(event);n”, ” }n”, “}n”, “n”, “mpl.figure.prototype.handle_save = function(fig, msg) {n”, ” fig.ondownload(fig, null);n”, “}n”, “n”, “n”, “mpl.find_output_cell = function(html_output) {n”, ” // Return the cell and output element which can be found *uniquely in the notebook.n”, ” // Note - this is a bit hacky, but it is done because the “notebook_saving.Notebook”n”, ” // IPython event is triggered only after the cells have been serialised, which forn”, ” // our purposes (turning an active figure into a static one), is too late.n”, ” var cells = IPython.notebook.get_cells();n”, ” var ncells = cells.length;n”, ” for (var i=0; i<ncells; i++) {n”, ” var cell = cells[i];n”, ” if (cell.cell_type === ‘code’){n”, ” for (var j=0; j<cell.output_area.outputs.length; j++) {n”, ” var data = cell.output_area.outputs[j];n”, ” if (data.data) {n”, ” // IPython >= 3 moved mimebundle to data attribute of outputn”, ” data = data.data;n”, ” }n”, ” if (data[‘text/html’] == html_output) {n”, ” return [cell, data, j];n”, ” }n”, ” }n”, ” }n”, ” }n”, “}n”, “n”, “// Register the function which deals with the matplotlib target/channel.n”, “// The kernel may be null if the page has been refreshed.n”, “if (IPython.notebook.kernel != null) {n”, ” IPython.notebook.kernel.comm_manager.register_target(‘matplotlib’, mpl.mpl_figure_comm);n”, “}n”

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” 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], “text/plain”: [

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]

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

}, {

“data”: {

“application/vnd.jupyter.widget-view+json”: {

“model_id”: “cd31ecab59334d92980f6c65b7cbc9ee”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“interactive(children=(IntSlider(value=0, description=’Frame’, max=79), Output()), _dom_classes=(‘widget-intera…”

]

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

}

], “source”: [

“%matplotlib notebookn”, “n”, “import matplotlib.lines as mlinesn”, “n”, “sta = processed.Network_Explorer.get_sta_entity_by_sourceID(14)n”, “n”, “images = sta.datan”, “num_of_images = images.shape[0]-1n”, “n”, “fig = plt.figure(figsize=(6,6))n”, “ax = fig.add_subplot(1,1,1)n”, “plt.title(‘STA Unit 14’)n”, “plt.box(False)n”, “fig.show()n”, “n”, “def updateFrame(Frame):n”, ” last_sensor = (sta.sensor_coordinates[1], sta.sensor_coordinates[0])n”, ” ax.imshow(np.transpose(images[Frame,::]), cmap=”gray”, vmin=np.amin(images), vmax=np.amax(images))n”, ” ax.add_artist(plt.Circle(last_sensor, 2))n”, ” for step in sta.axon:n”, ” ax.add_line(mlines.Line2D([last_sensor[0]-1, step[0]-1], [last_sensor[1]-1, step[1]-1]))n”, ” last_sensor = stepn”, ” n”, “_ = interact(updateFrame, Frame=widgets.IntSlider(min=0,max=num_of_images,step=1,value=0))”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

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

“### Spike Sorter<a id=’spikeSorter’></a>n”, “n”, “The Spike_Sorter data source contains neuronal units as the result of the CMOS-MEA-Tools spike sorting. Each unit is represented by its own group in the data source, while quality measures and other meta data for all units are contained in the Spike_Sorter.Units data set. “

]

}, {

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

{

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

“——————————————————————————-n”, “Parent Group: <class ‘McsPyDataTools.McsPy.McsCMOSMEA.SpikeSorter’ object at 0x246dcd71080>n”, “——————————————————————————-n”, “n”, “n”, “| Mcs Type | HDF5 name | McsPy name |\n", "===============================================================================\n", "Groups:\n", "| SingleUnit | Unit 1 | Unit_1 |\n", "| SingleUnit | Unit 10 | Unit_10 |\n", "| SingleUnit | Unit 11 | Unit_11 |\n", "| SingleUnit | Unit 12 | Unit_12 |\n", "| SingleUnit | Unit 13 | Unit_13 |\n", "| SingleUnit | Unit 14 | Unit_14 |\n", "| SingleUnit | Unit 15 | Unit_15 |\n", "| SingleUnit | Unit 16 | Unit_16 |\n", "| SingleUnit | Unit 17 | Unit_17 |\n", "| SingleUnit | Unit 18 | Unit_18 |\n", "| SingleUnit | Unit 19 | Unit_19 |\n", "| SingleUnit | Unit 2 | Unit_2 |\n", "| SingleUnit | Unit 20 | Unit_20 |\n", "| SingleUnit | Unit 21 | Unit_21 |\n", "| SingleUnit | Unit 22 | Unit_22 |\n", "| SingleUnit | Unit 23 | Unit_23 |\n", "| SingleUnit | Unit 24 | Unit_24 |\n", "| SingleUnit | Unit 25 | Unit_25 |\n", "| SingleUnit | Unit 26 | Unit_26 |\n", "| SingleUnit | Unit 27 | Unit_27 |\n", "| SingleUnit | Unit 28 | Unit_28 |\n", "| SingleUnit | Unit 29 | Unit_29 |\n", "| SingleUnit | Unit 3 | Unit_3 |\n", "| SingleUnit | Unit 30 | Unit_30 |\n", "| SingleUnit | Unit 31 | Unit_31 |\n", "| SingleUnit | Unit 32 | Unit_32 |\n", "| SingleUnit | Unit 33 | Unit_33 |\n", "| SingleUnit | Unit 34 | Unit_34 |\n", "| SingleUnit | Unit 35 | Unit_35 |\n", "| SingleUnit | Unit 36 | Unit_36 |\n", "| SingleUnit | Unit 37 | Unit_37 |\n", "| SingleUnit | Unit 38 | Unit_38 |\n", "| SingleUnit | Unit 39 | Unit_39 |\n", "| SingleUnit | Unit 4 | Unit_4 |\n", "| SingleUnit | Unit 40 | Unit_40 |\n", "| SingleUnit | Unit 41 | Unit_41 |\n", "| SingleUnit | Unit 42 | Unit_42 |\n", "| SingleUnit | Unit 43 | Unit_43 |\n", "| SingleUnit | Unit 44 | Unit_44 |\n", "| SingleUnit | Unit 45 | Unit_45 |\n", "| SingleUnit | Unit 46 | Unit_46 |\n", "| SingleUnit | Unit 47 | Unit_47 |\n", "| SingleUnit | Unit 48 | Unit_48 |\n", "| SingleUnit | Unit 49 | Unit_49 |\n", "| SingleUnit | Unit 5 | Unit_5 |\n", "| SingleUnit | Unit 50 | Unit_50 |\n", "| SingleUnit | Unit 51 | Unit_51 |\n", "| SingleUnit | Unit 52 | Unit_52 |\n", "| SingleUnit | Unit 53 | Unit_53 |\n", "| SingleUnit | Unit 54 | Unit_54 |\n", "| SingleUnit | Unit 55 | Unit_55 |\n", "| SingleUnit | Unit 56 | Unit_56 |\n", "| SingleUnit | Unit 57 | Unit_57 |\n", "| SingleUnit | Unit 58 | Unit_58 |\n", "| SingleUnit | Unit 59 | Unit_59 |\n", "| SingleUnit | Unit 6 | Unit_6 |\n", "| SingleUnit | Unit 60 | Unit_60 |\n", "| SingleUnit | Unit 61 | Unit_61 |\n", "| SingleUnit | Unit 62 | Unit_62 |\n", "| SingleUnit | Unit 63 | Unit_63 |\n", "| SingleUnit | Unit 64 | Unit_64 |\n", "| SingleUnit | Unit 65 | Unit_65 |\n", "| SingleUnit | Unit 66 | Unit_66 |\n", "| SingleUnit | Unit 67 | Unit_67 |\n", "| SingleUnit | Unit 68 | Unit_68 |\n", "| SingleUnit | Unit 69 | Unit_69 |\n", "| SingleUnit | Unit 7 | Unit_7 |\n", "| SingleUnit | Unit 70 | Unit_70 |\n", "| SingleUnit | Unit 71 | Unit_71 |\n", "| SingleUnit | Unit 72 | Unit_72 |\n", "| SingleUnit | Unit 73 | Unit_73 |\n", "| SingleUnit | Unit 74 | Unit_74 |\n", "| SingleUnit | Unit 75 | Unit_75 |\n", "| SingleUnit | Unit 76 | Unit_76 |\n", "| SingleUnit | Unit 77 | Unit_77 |\n", "| SingleUnit | Unit 78 | Unit_78 |\n", "| SingleUnit | Unit 79 | Unit_79 |\n", "| SingleUnit | Unit 8 | Unit_8 |\n", "| SingleUnit | Unit 80 | Unit_80 |\n", "| SingleUnit | Unit 81 | Unit_81 |\n", "| SingleUnit | Unit 82 | Unit_82 |\n", "| SingleUnit | Unit 83 | Unit_83 |\n", "| SingleUnit | Unit 84 | Unit_84 |\n", "| SingleUnit | Unit 85 | Unit_85 |\n", "| SingleUnit | Unit 86 | Unit_86 |\n", "| SingleUnit | Unit 87 | Unit_87 |\n", "| SingleUnit | Unit 88 | Unit_88 |\n", "| SingleUnit | Unit 89 | Unit_89 |\n", "| SingleUnit | Unit 9 | Unit_9 |\n", "| SingleUnit | Unit 90 | Unit_90 |\n", "-------------------------------------------------------------------------------\n", "Datasets:\n", "| Projection Matrix | Projection Matrix | Projection_Matrix |\n", "| SettingsPeakDetection | SettingsPeakDetection | SettingsPeakDetection |\n", "| SettingsPostProcessing | SettingsPostProcessing | SettingsPostProcessing |\n", "| SettingsRoiDetection | SettingsRoiDetection | SettingsRoiDetection |\n", "| SettingsSorterComputing | SettingsSorterComputing | SettingsSorterComputing |\n", "| SettingsSorterGeneral | SettingsSorterGeneral | SettingsSorterGeneral |\n", "| SettingsSpikeSorter | SettingsSpikeSorter | SettingsSpikeSorter |\n", "| Units | Units | Units |n”, “n”

]

}

], “source”: [

“print(processed.Spike_Sorter)”

]

}, {

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

“For ease of use, some convenience functions are available to access units and quality measures:”

]

}, {

“cell_type”: “code”, “execution_count”: 38, “metadata”: {}, “outputs”: [

{

“data”: {

“text/plain”: [

“[‘SDScore’,n”, ” ‘AmplitudeSD’,n”, ” ‘RSTD’,n”, ” ‘Separability’,n”, ” ‘IsoINN’,n”, ” ‘IsoIBg’,n”, ” ‘NoiseStd’,n”, ” ‘SNR’,n”, ” ‘Skewness’,n”, ” ‘Kurtosis’,n”, ” ‘IcaConverged’,n”, ” ‘IcaIterations’]”

]

}, “execution_count”: 38, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“processed.Spike_Sorter.get_units_by_id() # lists all units, ordered by unit idn”, “processed.Spike_Sorter.get_units_by_measure(‘Kurtosis’) # lists all units ordered by the given quality measuren”, “processed.Spike_Sorter.get_unit_measures() # lists all quality measures”

]

}, {

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

“Each unit contains 5 data sets:n”, “- Peaks: Contains all peaks detected in the Source signal, including peaks below the spike detection threshold. This is necessary for the automatic spike threshold estimation in the CMOS-MEA-Tools Spike Sorter. For peaks larger than the spike detection threshold, their ‘IncludePeak’ flag is set to 1.n”, “- RoiSTAs: Contains the spike triggered averages for the subset of sensors comprising the “ROI” of the unit. The spike-triggered average is computed by taking the timestamp of all Peaks where ‘IncludePeak’ is set, extracting a signal cutout for all sensors in the unit ROI around each timestamp and averaging these cutouts over all timestamps. The STA dimensions are channels x samples.n”, “- Source: Contains the dimensionless source signal for the unit in ICA space.n”, “- Unit_Info: Contains the meta data for the unit, including its quality measuresn”, “- Unmixing: Contains a channels x embedding matrix that represents the projection from raw channel data to ICA space. Creating embedding many sample-shifted copies of the raw data for all channels in the unit ROI and then multiplying this with the Unmixing matrix will create the Source signal.”

]

}, {

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

{

“data”: {

“text/plain”: [

“7.64561711669349”

]

}, “execution_count”: 4, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“unit = processed.Spike_Sorter.get_unit(14) # get unit by unit idn”, “unit.get_measure(‘Separability’)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

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

“#### Sourcen”, “n”, “The source signal has no associated units but can be drawn like any other sampled signal.”

]

}, {

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

{

“data”: {

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mmZ7HmzlzZqCEPOPGjUPNmjUTk/WWopVPD5byCQtPhtm+fTsWLFgQ6TxTTzQmT56MZ599NtJ5m2zixIlYubK4i7FOnTrh5ptvDm1ebp8quelEVIf33nsvtGkvX748/SQy29atW7H33nunCwtW9t9/f9SuXTus8EJx5JFHolWrVqHO44cffsCjjz6Kjh07lvmtX79+aNu2LcaNGxdqDH7EkSHRrZdffhlPPvmk4zDff/89AODdd9+NIqScli5dChHBf/7zn8jmOX/+/PS50s7kyZPx55/uexNRSqFx48Zo165d0PBci6M9X6pNatDttXXrVjz++OPGt/MlygcsPBlm4MCBaNiwYSSpj7OfaGS+duYr4mgU4lOlevXq4cgjj7T8bcmSJdi4cSN69+4dcVTuuXnIkDp+br75Zlx00UWRNCRPFaytqo/88ssvAODp5tVUpp2b3GSxPO6443DAAQe4ml7Qc0KqgOw225wOjRo1yvlAo3nz5jj11FM9T/vLL7+0rUZM//PII4+gT58+eOmll+IOpSCZdl6icLHwZKgNGzbEHYJxeHJKhq+//tqxqk3Se0pv3rw5brnlFsvfsm98+/fvz84NS6xduzZn1cWkJYxYtWoV7r///pzDTZ06NW/ae8yaNQuvvvpqme/dFG5++uknX/O8/PLLfVcjnj17Nnbt2uVr3Ndffz39ZtGNOK9RqXuGfM38+fPPP3tuV/Tnn3/izTffDCmiYlbnpVmzZmHgwIGBp92uXTu8/fbbgadD+rHwZIg4bwxyVbcwjZd1ZVqBa9GiRVqrhpi2fIsXL8Zpp52Gq6++Ou5QEmnBggWYP39+3GHk9MILL0BE8N1335V6Sz5nzhw0b97c8uFPu3bt0KpVK8vfTCwYubF+/frcAyWE23NJkyZN0LVr10jn7fcGcu7cuTjiiCNKZW314oorrsAxxxzja1w3lFJ46qmnQpt+3N566y288cYb6b/9Xq9WrlyJQw891HM2w4svvhidO3fGb7/9Vur7adOmhfowo0mTJraxLl261PUDxJEjRwaKY9y4cWXaav/3v/+FiKQ74TXB+vXrE1crgoUnyssnVabejNWvX19LqmdTl2/jxo0Aip+8kXcNGzZEo0aN4g4jp1TVoBNPPLFUB8H33HMPJk+ejDFjxpQZJ1W9LfvGwbQHAIXG5IQRQaUSy3z33XexxeBkxowZ6eQi+XQdXrt2LapUqYLLL78cXbp0CbyO161bBwCek8ukCkjZb6yaNWsWSye0f/31F+rWrYvu3bt7HtfPefKMM84o01Y7de7W3TF1EH/7299QvXr1uMPwhIUnQ0VxQ2HqDbhunTt3dpVF6YknnsD+++9f5vso+2+I29lnn225DqLmZv9XSuGhhx7y9ASxffv2jp1nvvfee/jmm29cT89ETutu69atpf7etWsX1qxZE2h+Jtz0iUjOVOtz5syJpC1ppr/++qvMU++osFBqlmXLluHXX38t9V3mg4RBgwb5qta4dOlSV/2KRWny5MnpDuBNFUf18VR19o8//jjyeZssie0ZWXiitMzCVD5deN9880189dVXOYfr3bu3ZYphL1mQNm3alG6c70Xc67uoqAjr16/HmDFjLNfBpEmTtGdxmj17dpnU204F+ieffLJUwebHH39Ev379cMkll7ie54gRIxwb0l988cW+GrVnC7o9hw8fjsaNG1teVLZt22bZL4ybhyFdunQp9RT2pptuwr777hso1qjZrdtcnfwefvjhOOSQQ2x/X79+vaf2LW7iOuOMM3DQQQf5nqYf+fBQ7LvvvvN1HjVZnTp1cPDBBzsOY5eFNMXqhr9u3bqoUaOG53gOPvhgx34IlVLo3bt3zpjy3bx58yAiOTu1jvsaHpbp06fjrLPOSvctScVYeKJQbN++XXvVreyT09KlS0v1rWKC888/3/EGLZspNzr9+vVDtWrVLH+bNm0aWrRokfPm1KsjjjgC559/vuvh77zzzlIFm1QjcJM6lta1Pa+44grMnTvX8oJ14YUXombNmr6nnTnNd955x/d0nCilcPPNN+OHH36w/M0Pv+v23HPPTe/bTol4WrVqpb19S64bLiqWvU+ceOKJaNiwoeWwzzzzDAYNGhRFWJa2bdtmuw+HeQO9YMECVKxYsVQboiB+/fVXx34I161bhyeeeAItW7bUMr+wDBkyJNT2MqkHfHZ9K5pyDQ9Lt27d8OWXXxZ8ITobC08J9v7771vWAf79998xc+bM6APKcMstt6BJkyZYsmRJzmE3btzo6RV66mR10kkn4ZJLLnF85avjYuYl8+HXX38deH5e6LpYO91EL1u2DAA87VP5+hTOBJ988kncIeS0Zs0a9O/fv1R1WTc3GWHsN59//rmrxA5+Cjq6bpyi7tvPShjrftSoUTjkkENsz+9+1t+tt96KXr16BQ3NlyVLlqBy5cp4/vnnI5936uY16uyd2fvF4MGD8dFHH2mZVtDxZ8+ejW7duqFz586O45lWrdGE66MJMSQZC0+GyL6IuNmxL7roIpx++ullvj/ggAPQtGlTz/MMKpU5aMWKFemqbm7aU+y9997o0KGD5/ktXrwYgPVy6Fy2Rx99VNu0dDE142BcT+E2btxYZt6//PJLrJ0+h1Gn3u+21L0P5NrOXuanlPK83xQVFRnfpsKLzOqC69atQ5s2bTBq1CiICHr27Olrmm63Qa51P3nyZM/t2jZu3Ijt27eje/fu+OWXXyyrmY4aNSqy9O269v9UIddNjYeioqLEtOUYNmyY44NOpRRGjhyJoqIi9OjRAxdccIGn6Qe9LtiNn2rDuWrVKjRv3hxHH310qd9T+23r1q0txy8qKvKc/tyJUsr47haiimHYsGGYN29eJPOKAwtPpM3s2bNx++23o1OnTp7HNbkBpUlPaILEYsKJ2y2vN99WT+5btmyJW265JZ0B0KtJkyahTZs2vtt6+cmolC21HnRuuzD356j2sd69e6NKlSqO/Ykl1VNPPYVPPvkEbdu2BQA899xznsbP3AaDBw929fbfzu+//47mzZvjuuuu8zTe3nvvbXvDmtK2bVuccMIJvmNz44wzzgh1+k7q1KmTM4OYCefkoqIidOrUCSeffLLtMG+99RbatWvnuu+iOJZr8uTJZaoJp6p22z1o6dGjBypVqlTm+6KiIssHBps3b8aKFStwwgkn4Lbbbivze8WKFXHUUUfljHX16tUYMGBAzuEymXQf4kanTp1w2GGHxR1GaFh4orSgCSNST9rD6vskaScPL3It2+zZsyEiGD16NIDgFydT16WX5co1bND9sHPnzvjkk098ZUtTSgXq3DDI9vWybeO4ydFRdee1114DYH9TlBSbNm0KrV+vVatWoUePHjj33HN9jT9p0iSsXbsWQHGjca/GjRuXc5hU+ztTz0dBrFixIr3+3NKxHrZs2eLpuEjNc+nSpbbDpN4QOg2TRC+88ILl93379sUee+xR5uHMKaecgv333x8TJ07E008/Xeo3pRR27tzpunr7TTfd5C/oCAwfPtyILKomY+HJUPl4MdHFTxVHnfMeNmyY1um5kUqXnuq3JOr5kztRrs++ffvioYceimXefoRVdSfJ2rVr56pfr/33399zvyypN6Z+UtF/9dVXaNGiRakOXJcuXeoqa6mVQrmeuV3OlStXhlYF9+6770aVKlV8TdvP/OIyZ84cbckzsr3yyisASrd3VkpZPkTIt/PSN998g0suuQS333573KEYjYWnAhbFQZ8PVYSyPfXUU5g/fz6OOuqoxPWKHYTVtrS7eJl6wU26zIxHjzzyCPr16xdjNPr52W/22muvECJxJ+jbercFohUrVuDuu+/2PH0vMuNftGgRgP91bAwARx55pGV/eU7n4STcWEYd4w8//IBatWrhxRdftPz9ww8/tEzIoLOdoRumb7vDDz8czzzzjOMwqWRHKbNnz8bYsWM9zcf09eCX3f6S6pA4SHXfQsDCk6HWrl3rK0NMlDetSinLDkX9NAAPS1jr47HHHsOMGTMwYsSIUKbvVtydKXfp0sX1sF7oTjgwYsSIRPZTkb0ewkotbmXSpEm+O5XNjHvLli34+eefHYf1u98opfKi3VMY58vsfWfHjh3pNiB+5u21CpppioqKtJ0vg0xnzpw5AGD7Fq9Dhw6eEzIkhZv1NmfOHHTq1Mky4U7Q7XfEEUegVatWgaYRRJT3RVbn7pUrV8ZSldRp2qlaNUnDwpMhsg+qFi1a+Or4Thc3B8zgwYNx6qmn4sMPPwRg1hMak2IBgFmzZmHIkCHapmfa8umis81Tyvjx49G+fXvcddddvmLyc/HYtGlToG0U5vbNXB6n+bRo0QLNmze3/M1uPKvvL730Uhx66KFa27fk6/6vg926qVixouc+e/LlDbJSCuXKlUP79u0DTUfHfnfZZZdpnZ4uOrd19nJ5Wc6uXbti2LBhparIRf3WLR/st99+Zb6rVasWPvjgAwDAa6+9ljOpSyYRQadOnbTusy+88AJOOeUUbdOLEgtPlOa1YXwqDWWqikfYTDlB+omjSZMm6Natm+9pmnSRTZrUG1yv+2mQde63kb4OOo8TP8kyso0ZMwbA/zJfBc2AZsp5wI+1a9fadkYdhVQXEl6FnaDG7TYNuu399k+U7euvv4aIeOoD0I3M5bNL+mBa9bwnnngCCxcu1BOMRtnrL+y02WGfl6ymH6Sz6G+//TadgMoqpb7V/HS29wbgWCPBdCw8FbDMk+by5cvLVMEyVZwJI3RjoSg8cazboG1gdM3bZJntaLwIa/mUUp7745k7d66vatWTJ0/OmQXSbjlz7U9FRUWe23OETfc269+/v9bpBZX9cEHnMV+lSpVSbeJyrcuoHmJmWrJkCXr37o3zzjsv8nnnkp163U/abDfbM87zro7OoqdOnYpy5cqlzx1JuY7EjYUnAhBeD9xeLyZr1651nbrXhIM8yQU3r0xeVjf7gpf4GzRoECQcbUxooxHlfMMe3kqrVq1Qrlw5T+M0btwYjRo10nYO0jGdZ599Fq1atcr5dsWuI0+338XplltuiTuESHlpD6LrrVo2p30g9dAh7naHVsfPjBkztE7P9OrGfvsj/PrrrwEUJ38Kq9uEfMTCE2ln1QD8oYcecuyEL+XYY491lbrXSyxhMKHgBsSfMMKOjr58dA3rJ/64q6GY0OYpKn4SRuiM22sK8BTTEiikOopO9cVjd1wceOCBqFmzZvpvq3WZ+s60wlO+MeH4s+M1trVr1/quhvXuu+9ixYoVpb77+OOPseeee5YqmMWxPzqth02bNmHz5s0RRmPt5ZdfRoUKFbBo0SJs3LgRF198sedpvPrqq1rvvZy8/vrrvjuvN0X5uAMgvZxOLn369EGDBg1w/fXXB5qOHaeTjNuUyjraWOSKJSgTTuC6l8+UJ+hhJIwIyu/21hmfaTdZXhJGULRyFXz8dHRqSpunuOVTlfFsmcuyfPly7LHHHp7GP/bYY/Hbb785PgyxWl8bNmxAx44d0aRJE8ycOTM9bqoPu1deeQWHH364p1iiEkY3Cddee63ncVJZWOfOnYt58+bhvffeCxxHWPv2pEmTcMUVV+Dyyy9HrVq1QplHFPjmyRBuL06LFy/GqFGjfM3j8ccfR/fu3T3P0xR2B3M+XcAoPF73k6QdH6bSvR51He9Lly5F7969tUwL0Nc+wtQHIybOe8GCBbj77rvzpnDmJk63y/LHH3/g2Wef9bzsY8eORe3atdNZdN3MUynl+ODTaT9IJZKx61eoV69egZPMREHXPvbSSy/Z/jZkyBD8+9//LvWd7qp2YR+zqUQey5cvD3U+YeObp5isXr0aU6ZM8dzQ8uijj8aaNWsSczEIQ+rgzqeb20LenjqY0lGnCQWFzHHmzZuH++67z7LPlKTRvW67dOmC8ePHa51mmKLKSJckbdq0wbx583DNNdfg4IMP9jUNnfuV7nUfpIrtlVdeiU8//RQnnXQSmjVr5nq8adOmAQAmTpxY5rewa0CYxqTMhkopy4y9jRo1QvXq1YOElXO+VBbfPMXk7LPPxvnnn48tW7Z4Gm/NmjUhRRQOuwOvW7dueOSRRyKORr+gJxa/J9Owq9EkSdKfOv/xxx9o1XWR6oAAACAASURBVKoVVq1alf7Oz/a1Gufqq6/GsGHDMHny5PR3prV5yhRlwojt27f7HjfFpPUXRpUy0xOW+HkoYHJhSafUvULmOtqwYUPObI9uHXbYYaGn/3YrikKOyds6lfArzBhNOteZwNjCk4gsFJEfReQHEZlq8buISH8RWSAiM0XkmDji9Ct10jH5gPTLTQPwIUOGoG/fvhFFpJefBu5B+U1fnGsYL8uhM4mDjvFzxZ6Uk/1zzz2HsWPHYuDAgXGHok3YVSSTsm398Lpsb7/9NkQk3eDe77qxKlRH9XAoH6+DbkS9H1etWtVzP2N222bevHl44okndITlOB+vw+iWz+cawN+9xa5du9J9RIUx7yQwtvBU4nSl1FFKKat3zucCaFjyrxuA5yONLA9k7rg6nlrGdSA4xZqvF2VdbyZ0jxdlwgiv/O4L+boPBeElYUSubVpI6zeM/TvVBmLu3Lm+xo+jHZZuSdyHTI85yDYPcs0wdV8Lq+rz1q1bjdoX3Kz/J598Eq1bt3Y9zV27dqXbtuUL0wtPTtoBeE0VmwigmojsH3dQcTPpINQte9lMaedioq1bt2Lo0KF5vT9kCmNfMGEfMr3aVByCLsu2bduwZs0aI7avF2zzVJYpNQCcjBw5UmujfhO3byHsm4MHD3b83c+yrVu3DrvvvjsefPBBv2HF4pdffvE0fMOGDctkb0zyvgCYXXhSAL4QkWkiUraVHFAHQGZ6lqUl3yVS0i7kQHw7fxLXVS66+yq69957cc0114TWcaJJTDoJm5LyPXs6mevIpPXlha52CK1bt8a+++6rIySjzkUmxZIt7n0urPlnT9dqPu3atXPdf47T9EzevrnojP0///kPmjdvrm16TsJuS7V69WoAxf0e6Ygl7uPMzm+//ZZuY5rk/TiTyYWnk5RSx6C4el5PETk163erLVBmzxGRbiIyVUSmpnZU0supyp+pB3MQOpcvrBNJqv3Dhg0bQpl+2HSlffY6zXzgZTlNu5C5jT1I3GFl2DNt/zIhHtP2r0LkZT/48MMP0adPn0DzO+aYYzBz5kzP47mN8/rrry+VAIdKS1JthSSfH4wtPCmllpX8vwrAhwCOzxpkKYC6GX8fAGCZxXReUEo1U0o1q1GjRljhJlI+9ScStUJaVj9M6Fg2zGmayMTljKIwlCRRLKeOhBFO3wVllTE2igQzJgpzf/Az7Q4dOqQ/+12n33//vadkULrbPD322GNo2bJloGnoiiVl6tSpxu6jn3/+eam/C+VcHJSRhScR2VNEqqQ+AzgbwKyswUYCuKIk614LAOuVUsnudcsgug70KNLCFmLCiJQoly+Mt0G6OM03yTdmpj5FzIeEEfl6k5Ban0EKrtnVPXWuq3POOUfbtOLehnHsu1HP06TkUW5iueuuuzBhwgRt89SRMfK4447D88+bmdPsp59+cvzdarn8bt9BgwbhzDPP9DWuaYwsPAGoCeBbEZkBYDKAT5RSn4tIdxHpXjLMpwB+BbAAwBAAN8QTan6I+yLkRSrWQkoY4fXpnM5U0UldlyZn/vM6b51JL9zsGyNGjPA1vyjke7Vgv90SeCnQRi0z9h9//DHGSJIrzuQYuvrSyxxu1KhREJFS/dv5lYTzwOzZswN1OeKG6f089erVS/s041I+7gCsKKV+BdDU4vvBGZ8VgJ5RxpUESTiJJF2Sbt6SfHJyy6RMT37Wd5z7kFW848ePR/v27WOIxplT1wpxMaGaqZ838lHxsiwrV65E9erVUa5cuRAjKktnVwy633KbsA0z+X0LYzXes88+CwDp9lGmLWsUTDmPWSnE7eGFqW+eCk4cB1EUr9bz9QDUve7ydT35pbOKYFwXKJ1v/8Kcb7Y///xTSxxUVhRtXKKoKq3bunXrUKtWLdx+++2+xtf1ZoSixwKEO1HFEtX2MGnd+sHCU0ySvuNYievJcJLXZZx1w02U5D6ZwqArc16c7SS8bJso4+Q+4368oOvKbUxeu1aIexuamm01U5jHVBjTjnubmhJDVJJ6rxA3Fp5iZupBauIB5aYqQL7SXagIu3pIEvafsMcLwrT+otxO12+1RZ0Z4uISdix+2zzlEmah208CEXIWVQZLL9s+quNQKWXUMZ8pSG2JJLR5ymT69SkKLDxR4sT9JNvNNMM+icVx8jI1214cN5VR09mWItdbIZOW20qQwpZpTF2OpFR1DTpekpi4rySxiqhXYVanDmOb6qoubur2MAULTwXMxJOxLqy3q4+p+4mOFLK6mbquTJDk7Id2vMbE6sx6xZmBDoj/bWPSRFWAIP+4Pdxh4SnPRNk5qclZnsJmesKIpCft0PH0LOobnTAkrTqH6TFk4k1Cbvm0/0UhaFW3pK2nMKoQmnRcJm17+MFkK/6w8GQIk04YXjlleYriwEvywR12xji7JB6mrjMTUj+bIEjMfpM16BRVwghT9+MopdZB0IcFYSSM8FotNKztaep+ksRzkxtxJqjRwcTtEndzAd0JwZJe/ZqFJ7JkYhah7JsEE24M42bidgLMWkdBhd0OQ9e68lONUXebJz838H4uokm+6Obitw2C3yQe+bAu8+l8k4uJDxh0zCPuwkFQJiSM0MnEmEzCwlMBS9pF0+4Jq5tx8o2JiRkAc+PyI+72FG5/i0M+3JCb0LdemDFEdUMbJx3rT9db3rDHN/EBQ5Csm36u53GIom82k+RDdfcosPBE2plwsMWdMCLuagQmbIMgvBbUvFysw2RqWzgT9gdd6c1NWJaUMKrCmnBDFdd+bNK2NYEJ68MpPbiO+EzY36Ngwra0EnbTgXzFwhNpU0jV6Ey9Sc6m48QY5XZlm6diulPRm3DhNrWKab4wIe1xtiSmwneSK/Yo30SFLci2C1LYCnsd5zM/68bPOEk+hnVh4ckQYfYlEOX8s0XVgDFKOpeJJ6HwFdrF1kv2pLBubpkwInnCSBgRlbj2gzgeCES1rFFf50w8luOuQULmYuEpT/g5KMPMemTiU9B8ZOo6iTIuu3npqrtt6jrOFkcWMy8x2IkrYYQJbZ68iPvtdBR0dFGgY7omMmk7ZQuyfk1swxx1oVZnsox8quptMhaeYsId0z+TLiJxncxNrdoQVxW/MKod6n4g4YepF0IT1k0+0PmWL4yG3qbuf+ReIaz7pC6jiedEJoxwh4WnmJnUJkEXk5YlX5/WxtHI04S3SUkS1dNY3dMJ6+0Ib8TDpfNYj3P/i5qJ6y3FhHNI1EyLO+4mFabMg+2RS2PhibQxqQ1S3CmATb/BCzNFbhBxb7c45hdnXGGtb6+ZEL1OM8X048wPk24owkiAEEb1UlPaPIWZMMKUZQxrWqYey2zjXFa+LEcQLDwVsCgOgKQ9CYlLIVSzC0uudRd0GU2uZ++WqQVHv+OZ2OYp7H7Q4kymYep5wqS+2EyYnk462yKZks3VqyQm59DFxJhMwsKTIZL0ajiOecUxvyiEVf0u7nUVdZ9K+VqlIGjqWZNuLu1+85MwIl+Eke0we58JklTBxMRBuplUY8JJEtZlvogqYYSJ29TEmEzEwlOeCLrDBxk/6qdKJh3cphdSTLp5Jve8JghxO4yfeQeZV9zHRyHx29A7yn0rLF7S85MZ/D4YKtRtmQ/NBbKZfl5xwsJTnjDpoMn3N1ImPCmPe/5hC2ObmnSMmCKKNk8pYSSMiHubxpFiPcp5hLF+dbZ5ivs8GPf+54bfgmXU1ZXtskZSuOJ6Y5aEY8cJC08FrBDqbyf9APUrjBvMqKvimTzNpImzwBHGGzET2zwFEWTZwizg6LqhLZTzsO7ljDrbnlPVWh3fe5mnKXQ2qUhSO/NCOWb9YuEpoZKyY0eVqSdb3Cdk3Y27dfRV5Ld6ZdzrMpewL0pxHmu62pxEfez4zYCZlPOabjqq5Jh4nJpQENQp7AeOJlbNctOGMu7MoiZMN0gMJu337OfJHRaeyJLJDYVNvEnwK5+WJWph35jF+bQ9qfuF34bWSV3eoEwq9Jtwk7R8+XJf4+VDGu9cTGy/GlbCo7hFFa/ut/JhVsFjrZDSWHgyRBKz7ZkSQ+b8duzYgR49emDZsmXapu/0JCbu9R33/O2E2d+JV1bH1oYNG7RNP9e8ctG9DXfs2OF6WB1vNONk0v6vK1V50G1iershU/YdKivKpDPZ3FQRNel4NwmP4eix8BQTnSeBzZs3a63fnBRW8X/22WcYPHgwevToEfm8/fC7H+Saf1L7jYpqn3zwwQcjmY8XutbdggUL8J///KfUb3YF/iR2baDrTZUp09DFb4pyL9OMezp2TD0f5ju3bZ64/pMn7G02YcIEjBs3LtR5hImFp5j5vcBl7th77rlnbHGEPS2vkpqlLdc6S/obgjC5vZnO3I5FRUVhhqSN331v/PjxALxv/wkTJuDXX3/1NU87cT7NTgpTz79BE0aEuc22bt2Kn376KbTpW8l13ogiYUQSq2OmJPEYTlKSBx2i3Eb//e9/I5uXbuXjDoDMZOITR5NOMHHzc4Jbvnw5Nm3aVKawfe2112L+/Pk5x0/a+k/ihTpuLVu2DG3a3B72dCYKSA2X723u1q9fn/4c1bLu2rWr1N9Rtg2OIi1+5jhe9qOorg1Lly5FjRo1IpmXV35SwpvQ5on84ZsnCoXJCSMWL16M7du3+x4/jItJFE8sL7nkEpxyyillfn/ppZccp2XqjdSSJUsA5I5v586dUYQTirDXfVjT95ulKsrjJo6uGrKHCfONk9e3RrrOQdkFjGymnk/cSHLsJnDax9zuf6tXr9YVTk5JKYxwv4weC08JpeOgNuHGKer5bdmyBfXq1cNVV13lepwwG7AGzXSTq8559njff/+9r/l7pXsfsLpgnnXWWa7GfeCBBwD8ryqbLqNGjSqTmMTUi1jm9li6dGng6dktp99jJYqn6iYK8hAnJWhbJ93r8pdffinzXVJuQgFg2rRpcYeQeNkPrKz2Mbt9woRj24QYvMhc31HUGkra+gmDcYUnEakrIuNEZI6IzBaRmy2GOU1E1ovIDyX/7o0jVl1mzJiBH374Qcu0XnvtNS3TAYDRo0djwoQJtr/7vWjfeeedgeLyOr9MW7duBQB8+umnkc87U9yFLi/i6l9r5syZ6afY++23n+1wudo8LViwAIC3LHRutG3bNv0mzy6uoLy8Pcgc5p577sGmTZssxzv++OMDx+VWGGnITboRd1q2sWPHapmO32no3P/8jhPVTVZRURH+9a9/larK51ezZs1cD5svffvoluvtYyaTEpvEZcmSJZg5cyY2btxoO4yubJ5ep5H0dRsW4wpPAHYCuF0p1RhACwA9ReRwi+G+UUodVfLvgWhD1Ouoo47CkCFDAk0jtcNff/31OkICALRu3dpVG4jsg+3nn3/G5MmTbYd/8sknPcVRrVo17dV54j4hvPjii7HO348onzatWLECTZs2xUMPPRTZPP0Imlxh69atWLNmTZnvdazrp59+Ov05imqhmazi97pMX331FVatWlVm/EGDBrle73FmE2zVqlWZYexu7r0UOObMmQMRwbfffusqDqfpTZkyxVWBw8u2i2qdZ8b0wQcf4I477sCNN94YyrxSVYRzMTFhhBc6EliFOU5YNm/eHHcIGDlyJJo0aRJoGibuG/nKuMKTUmq5Ump6yeeNAOYAqBNvVPkv+8DQUZ0E0HMwr1+/HkVFRZg5c2bOYXM9NQnS/4ROX3/9te1vu3btwjnnnKN9nlFr3bo1OnfuHGgabqrQpN4shUXn9v/mm29wyimnpN+CnX766Rg0aBAA4IsvvtA2H6C4KodTtc4XX3xR23EOFF/8v/nmG23TO/PMM3HqqadCKVWqeqSXp9pOy+fnXFBUVBSo+qeOp8epN1rDhg1zHC/XvHbs2IHjjz8eb7zxhu0wum6Kw34bdf/99wOwL5wGdeCBB7oabuzYsaG9dYmiYGNX/Sto1kUnqQchcRamDjjgANvfomq/rZTCokWLQpsX6WVc4SmTiNQHcDSASRY/nyAiM0TkMxH5u8M0uonIVBGZGmVDw7CFfaL566+/XA+7YsUKx9fNOmzbti39OftktnjxYnTp0sVxmLCEUb942bJlZW6kvS6P2yeludx0003pmwGvyzp69Gi8+eabWuIIm1IK//znP0t9Z7XOg7aHuPrqq/Htt99i4cKFAICJEyemf8vumylMw4cPx3XXXYcVK1Zom2a7du3SF39dCSPmzZuH5557DnXq1LF8eBL1DdeAAQNw+umnhzqPqKr0Zqfd1lHAWbFiRamaBWGeh3W3YcylfPnyrvqlyZWAx4nO/dmklPVOst8WxvGGY+3atZbfF0K2YLv7YrZ5cmZs4UlE9gLwPoBblFIbsn6eDqCeUqopgAEARthNRyn1glKqmVKqmakpLqPWtWtXiEigKkcbN27Eli1bAAAPP/wwGjdu7Gs6P/74o+8YUgdwr1698MYbb+Dzzz93Pa6XJ5TZJ4qVK1di4MCBjuO4rTYYVtupVLXJoNMfMGCA6zciqQIBgFJP0HS3NXLLy7IvWLAA//d//5dzuFztIeJo3zNz5kw888wznuIJ6wm9X/PmzQNQujCZkrphTQ0Tp59//jnQ+DrbIQ0cODBQ5rGPPvrI9bzc6tSpE15++eX033PnzgVQ/BZRt1QimEw6j7/s89auXbvw6KOPlvrut99+KzPehg0b8Oqrr2qLw2RxZ5UNSxyFg1zrZvr06aHMN3s/t1v27H2/0BlZeBKRCiguOL2plPog+3el1Aal1F8lnz8FUEFEqkccZiS2bNmCxo0bO1bzArxVZUmd2K3q5Lu19957Y+jQoem/f//9d8uD7rPPPnM86I888sj05zZt2qQ/ZxeE7Or3e/Hee++5PinOmDEj/Tn7pPbnn3/mHD/sNlep5XBT7SqqC1aDBg0wYkTZ5xgVK1ZEUVERHnnkEdsnfCnLly/XFo/TdspeJ4MHD/Y1j7AyMXq5eDdt2hS33nqr1vnn4uVNs5tlmTJlSqDxraxfv94xoYedlStXuh428zwRhJtlnDVrVpnvFi5cWKajSbfb3aqgamfDhuznl9bsCuU6j+uUsG9wr7vuujLfZa/bK664wnLcrl27hhGSb9988w3ef/99T+PoTBTENxXWvKzj559/3tX0Pv744yAh2XrrrbdCmW5SGVd4kuKj7CUAc5RST9kMU6tkOIjI8Shejtx3tAZxe9D89NNPmDt3Lm677TbtMWzatMnT8N9//z1EJP000Y0HHngAxx57bPpvp7cQn3zySfrzueeeW+o3r22AUnW3MxvjX3zxxa7H9/pWbseOHaW2qZe3YG7Y3aSl6voDxU9Bv/zyS63z9cquoDxmzBj07dsXN9xwg+P4mfsK4P+ia/cmJuXpp5/Gbrv97/T31FNlTzVuqrR5LTxlts+yShQRhFMs2YVsLxftfv36oVOnTqW+053OOTOe7BtwLw+GMo0YMcJXoczuhthKjx49PMdlxe32yD5/Zhacvvvuu1K/eS3YTZ061fY3nW/9ch3TQZ6w20170iSrmv/2lFK+3x4FKShcffXVlt9v27Ytve39PBQ59dRTcdFFF/mKadeuXejfv7/jMG5jcipEB3nYY/VgwUTZzSEysxnrethlV521adOmrsbPbPf26KOPYvbs2WWGScobxDCVjzsACycB6ALgRxFJ5e++G8CBAKCUGgzgIgA9RGQngC0AOqmEbs0os9sEnXfqycN7773ne56XXXaZ73HdSF14U9Upsqu1WC3zhAkTcOWVV/qa36uvvoqmTZvitNNOwx133JH+/pprrnGcZ/b2mz59OiZMmIBTTz3Vcj6PP/54zlgOOuggy2ln++qrrzw/hQwqdfPupS1dEFZvYrxKZXpze6x5SU6ilMK+++7raroTJ07Egw8+6GrYbG7fGDhJZTx85513Ak/LTua6a9SoEapUqZL++8MPPywzTIpT275c282uOqpV5q3ly5dDRMpsU68NvO1iynwLlKuarFMbMSt2MWaP+9prr5UpMHi9zrjpcmPdunWOx0b2AxQ7uqqeeumDSDenZB2ZcVSuXDn9ndMDAbdSVe7d2LFjh7aOae+++27b33bu3InnnntOy3zi4GafqVOndO4z3R24X3jhhba/uUm4BQAdO3YEULw8dtsrV7OFTDr6FTSRcYUnpdS3ABzv6pVSAwG433oJljogs5/GeakG8fPPP1s2cs68aKT6P7Kyc+dO3Hnnnfjjjz8AFLdx8itIwStFRDBmzJj0jXjmSSvzTYwbZ599NsaMGVPqu5YtW2KfffZxNf706dNx2mmnASj9Wj1X1T6rp+ktW7a0PAFnN+wG3N84WN0YeG1/kJqGm4tDZtIOK07TmDNnTpnvnJ6GZ2rYsKGr4fxwWqbM9evUAN+r1EVVKYUTTjjB93TsYg+7HVpq2Xfu3JkuONvdMGdfiFetWlUqRXn2NDOdccYZQUN1pXbt2gCAnj172g6Ta98HrDuQBVAmWYmd7O1m9ZAgjL5eTGRVcPYT944dOyzbLlnx2t+akzVr1uRsR/LJJ5+gb9++pb7zWmi0Om569erlaRphsFqOnj17onx5s25L3bbLdvOm1+kaYcrzf901Il555RWt0zOFWXspOcqsy1qvXj3X4w0YMKBUqt+UzIu90yvd0aNHl+ozxq6gFWVfCWeffXb6c672YE6yC04AHDsGduKlGuTw4cPx7rvvlqlmt3nz5jIFhnvvLdsHtJc2J9ltIjK5eVLs5YbE7gml1c1wtuzUy0Bx5kGnPsOikCocW8leN5k3YVaFXrd0XMCcLsZ+bp68PLC56aabABQ/4fbylNtJVI3T/VYbGzNmDF5//XXHYYJWvz7llFNyVit0Ot4zuXkLnLTUyX/++afn8/fMmTPTb+1T7PYdNw8d3J4v3bx1mD17NqpWrVrmezdvgpzi8FqNMWXkyJGl/g5yw2937dH9NiYIpZTrt2G5HtxG3WdXJjdZIskb49o8FYKtW7f6evJrVQCKgtVNrZWjjz465EiK2bXrcVO1zSQdO3bECy+8UOq7rl27okOHDqW+y5UVq6ioyPYG9a677goWpEd2J+lrr70WgL+LRfPmzQPFFJTTW9lMb775JubPn6913qNGjQo0vs43CKm3L2HPx46XfWf8+PGlHvh4EeQhkJtG3UFMnz5dW7cQVh1177///gDMeQru1YQJE1x17J6L3fnUTfs7t8dCkGPmvPPO8z0ugFLtWOyqAVo98Mp+C5NqX5OqlWInCW8yL7/8cgDFb8TcvokMKjMJQ5jHXFRv6K3069cvtnmHSilVMP+OPfZYZYKxY8cqAAqA6tq1a/qz1b8+ffqkP19zzTWOw2b/+/jjj5VSSvXs2dPTePn876qrrkp/3rlzZ+zxZP878MADy3xXv359T9Po0aOH1piOP/54BUCdccYZsa8f/rP/98gjj5T6u1+/furoo4+OPa7Uv7322iv2GIL+S+q5VCnladi333479pjj/Pfkk0+GPo+WLVuGOv0TTzxRAVB33HFH7Oszqf9++OEHbdPyev8W979TTjnF9bCZ55cUP/M0DYCpyqY8wWp7MctVH/Sxxx5Lf85MDe7GpZdeqr3+atJl9kFSv379+ALxwEvqZED/0++4q82RO1ZVxpLwxDdJBg0aFHcIvowdO9b1sJ06dUK5cuVCjMZ8bvp8C0qF/HYvVX3TbXs6Kuuoo46KOwQyFKvtxcBvGwCvJ9uNGzeiQoUKia2CETYTs8BYbStdbUYov2UXlJRSkbZDzCWqTItUlpc+/YYNG8Y+XSLgt20tJdNLL70Udwie5KqKmem1115LfxYR3+3Qk3SvysJTAUhy+k8yx1dffRV3COTgp59+KvX3lClTPPXJRkREBFhnv7WT3dWLU5IlJ9ltwE3GwhORQZz6rSHyYvTo0XGHQERE5ErU/U8GwcJTDNgOgYiIiIioWJKqdrPwRERERERE5AILTzHgmyciIiIiomJJujdm4SkGCxcujDsEIiIiIiLyiIWnGGzfvj3uEIiIiIiIyCMWnmLAwhMRERERUbGioqK4Q3CNhacYvPzyy3GHQERERERkhIkTJ8YdgmssPMUgSaVrIiIiIiIqxsJTDHbbjaudiIiIiChpeBcfgySlYyQiIiIiCtvmzZvjDsEVFp5iMGfOnLhDICIiIiIyhlIq7hBcYeGJiIiIiIhixcITERERERGRCyw8ERERERERucDCExERERERkQsrVqyIOwRXWHgiIiIiIqJYLV68OO4QXGHhiYiIiIiIyAUWnoiIiIiIiFxg4YmIiIiIiGIlInGH4AoLT0REREREFKulS5fGHYIrLDwREREREVGstm3bFncIrhhbeBKR1iIyT0QWiEgfi98riciwkt8niUj96KMkIiIiIqKg6tSpE3cIrhhZeBKRcgAGATgXwOEALhWRw7MGuwbAWqXUIQCeBvB4tFESEREREZEOFSpUiDsEV4wsPAE4HsACpdSvSqntAN4B0C5rmHYAXi35/B6AMyUpLc2IiIiIiCgtKbfxphae6gBYkvH30pLvLIdRSu0EsB7AvpFER0RERERE2iil4g7BFVMLT1ZFz+w16mYYiEg3EZkqIlNXr16tJTgiIiIiIio8OQtPIvKlm+80WwqgbsbfBwBYZjeMiJQHUBXAmuwJKaVeUEo1U0o1q1GjRkjhEhERERGRX4mvticilUXkbwCqi8g+IvK3kn/1AdQOOa4pABqKSAMRqQigE4CRWcOMBHBlyeeLAHylkvK+j4iIiIiI0pJyG1/e4bfrAdyC4oLS9IzvN6A4E15olFI7RaQXgNEAygEYqpSaLSIPAJiqlBoJ4CUAr4vIAhS/ceoUZkxERERERFTYbAtPSqlnATwrIjcqpQZEGFNq/p8C+DTru3szPm8FcHHUcRERERERUWGyLTyJyBlKqa8A/C4iHbJ/V0p9EGpkREREREREBnGqttcSwFcA/p/F7AxmwgAAIABJREFUbwoAC09ERERERFQwnKrt3Vfy/1XRhUNERERERGQmpzdPAAARqQTgQgD1M4dXSj0QXlhERERERFQoioqK4g7BlZyFJwAfAVgPYBqAbeGGQ0REREREhWb33XePOwRX3BSeDlBKtQ49EiIiIiIiKkhHH3103CG4YttJbob/ikiT0CMhIiIiIqKClA+d5KacDKCriPyG4mp7AkAppY4MNTIiIiIiIioI+VR4Ojf0KIiIiIiIqGAlPmGEiPyt5OPGkv8VgHUqKcVCIiIiIiJKhEqVKsUdgitOb56mobjAJBnf7SUiMwBcq5RaGGZgRERERERUGPbcc8+4Q3DFqZPcBlbfi0gHAIMBMAMfEREREREVDDfZ9kpRSn0AYL8QYiEiIiIiIjKW58KTiOzlZzwiIiIiIqIkc0oYcZvF1/sAaAtgYGgRERERERERGcgpYUSVrL8VgBUAOiulfgwvJCIiIiIiIvM4JYy4P8pAiIiIiIiITMa2S0RERERERC6w8EREREREROQCC09EREREREQu5Cw8icgBIvKhiKwWkZUi8r6IHBBFcERERERERKZw8+bpZQAjAewPoA6AUSXfEQVWt27d9Oe///3vMUZCUahUqVLcIRB5ds455+DOO++MO4y8ICJxh0BEFIibwlMNpdTLSqmdJf9eAVAj5LgoQ/Xq1XHiiSfGHUYoTj311PTnTz75JMZIKAq8cQrPG2+8Ecp0H3/88VCmG7arr75a27Tuuece1KpVS9v0/KpZs2bcIQTGcwBl+r//+7+4QyDNhg8fHncIoXNTePpDRDqLSLmSf50B/Bl2YPlsr732KvX3ueeei3vuucd2+NWrV4cdkhHq1asXdwgFIR9uwJKqZcuWoU27fHmnbvv+x+sb3tQblxYtWniOKYhx48Zh2bJlvsc/++yztcVSsWJF9OrVK+dwp512mrZ5WmnUqFGo0w/Lr7/+GncIgbBWhH7t2rUDULr2CZmlefPmvsZLbVsAePTRR3WFYxQ3haerAVyC4g5ylwO4qOQ78kkpVerv888/P1HVmTp27JioeE115ZVXxjLfoUOHonLlyrHMO84L5Q033KB1eldccYXncY444gjP41SrVs3VcJlP9P3Mx8n06dPx+eefa51mLqeddhr2339/X+P+9NNP6NixIwYNGqQlluOOOw4VKlTARRdd5DicmwJWECKCjRs3aplWUVGRlum4sd9++0Uyn/fffz/wNPbee+8y340bNy7wdAtJuXLlXA/LN5HmGjNmTPqzl+1UoUKF9Oc+ffpojckUOQtPSqnFSqm2SqkaSqn9lFIXKKUWRRFcocguTFkJ+6J8+OGHux5WRLB161Yt861SpYqW6aR07dq11N9RFPJOPvnkMt+1bds253iZVRajctVVV+G8887DvHnztE+7SZMmjr/feuutxtyE6Die/Fz0/RRaFy5c6Gq4zHiuueYaV8O5dfTRR6Nq1aquh7/00ks9TT/7uA2qcePGANwXPHNxu86OOuooLfNziiO75kKQaTVo0EDLtDLdd9992qfpxqOPPor27dsHns65555b5rsaNWrg888/d/1msUePHjmH6d69u6uq6p06dXI1Ty+uuuoq7dMMKvOG24mu/T/Tzp07tU/Ti+OOOy70ebi5z8ym+/4sn9gWnkTkXod//aIMMt/42YkvvfTSUuP94x//8D3/888/v8x3Bx54YJnv+vfvX+a7SpUqoW/fvpbT3WOPPTzHcv/99wPIfVNZu3ZtV9PLXrdRvGE56KCDynx3+eWX5xzP7Q2jrrYWr7zyCoYOHep7fKv9JlOuuutPPfUU6tSp43v+2Z555hnf4w4YMMD1sDVqWDfx9FMI6dy5M+6//34ccID7hKVeCi0plSpV8nU8+nXBBReU+vuEE06IbN5O/va3v2md3rPPPlvq7+yHAWE/RXczfS8FokceecTx9zPPPNP1tADgvPPOc3VtWrlypafputGnTx/b9WNVILJjV0A655xzcMcdd7iaxsCBA3MOU65cOV8P9/ycD7JZXQd233132+HtrqMVK1b0HUP2tXq//fbDli1bfE8vCXI9pA6jYBgGL8dTvnJ687TJ4h8AXAOgd8hx5bXsk4afwlSQp3sHH3wwgNJPp60uOtk3jS1atMDWrVvTVYJuueUWAMBhhx0G4H+v6r20jUjd4OV6ze/2qZRSCgsWLEjfFNSvX991LH7luqH55ptvLL/fbTd33awtXrzYU6N9u7Zj++yzj+X306dPd7zJ/Oc//wkg91OozOWxK3DoFPRp3Y8//uhquBEjRlh+7+dGuVy5crj33nsxdepUT+Odcsopnufl57wSl7AS4uhug1S7du1SDzOy25lFVQVp+/bttr95qSLXqVMndOjQwfZ3r8vz0UcfuZqOjgJAWK6//no89thjePjhh31PQ+ex16FDBwwfPhy33nqr72m4uV+wux451SiwOy952W8yh42rOnkU2rVr51it9JtvvsGcOXNCmfcLL7zgetjnnnsOQNk3nm636fXXX+8+sASzvXtTSv0r9Q/ACwB2B3AVgHcAlH3UTq55ObF+9tlnIUbyP1YHRvZ32U/JUic6qwQEqRsMt4W87HVi9WTMqnqclYMPPhgnnXQSAOv66264Xe/vvvuu5VuxzOVxG7edChUqeHrCN3nyZMvvM7dnKr66devi6KOPdpye27d+mdM/4IADSrWpGD16tKtp2HnnnXdsf/N7I5Z6iJCLXTstPzfKqfVes2ZNT3XB3cYal+x93Mu6eeCBB0JrExPGzZjTslWvXl37/DJdd911AIrPCXZV1OJsQ+I2aYnJRAS9e/cO1FbDb+Epu/D58MMP4+KLL8ZFF12UPk/vueeenqd72223eR4n89oZdnVUwP06C+OhkI5j5tVXX3X8vXHjxo61AU4++WRPNRK8SJ033LCr6uy0jp544on058GDB7sPLMEcH32LyN9E5CEAMwGUB3CMUqq3UmpVGMGIyJMiMldEZpZ0zGu5FUVkoYj8KCI/iIi3R7gGcjoZtG7dOtC0x48fb/tbrhOGjhPK9ddfj2+//Tb9d8OGDV3NI7tO9iWXXJL+3KNHD9vqDrna3aTUq1cPjz32mO3vXtoj/eMf/8Arr7zievgw1alTx9ONqM4brQ4dOuCQQw6xnHbQzGcdO3a0/c3qhs3qKarui66fdZf5hi/MLEROy5or7sxt6NZtt92GX375JfD5ykrUb9DOOOMMx9/t4rnqqqtKvZ31217A6W155vnzgw8+sIwl6Q3wo0oukXLQQQeVahifEsd6zN6eVjUyTj/9dFfVAlPt/gDnh4ipKubZy5v6WymFLl265JyfE6sq36ll9bqeTS2gZ65vr0yuJZB6OOl0b1WI6ead2jw9CWAKgI0Amiil/qGUWhtyPGMAHKGUOhLAzwDuchj2dKXUUUqpZiHHFLowD5wgqZHbtGmDCy64AC+/HKxP5NRbIKC4qtT69es9jb9u3bpSBZ1LL720VMKK1EH94osvun7CJiLasq9VrFgRnTt3LvVdrm3qlEXsrbfewnvvvecrliieENqpXLkyhg0b5nv8m266Kf25cePG2LhxI7777juMGjXKcbxGjRqVqWLQr1/4zTL79u1bKpWrmwuI27d4KXZVPjNdfvnlmD17dpnv3dyUWFWH9VMlWERKtf3ze+P5//7f//M1HuAtw5cTXV0mbNiwQct0MnlpM3jxxRdrn78OufaN7CyWTm1xdDnrrLO0Ts/N/u/3ui8i6Nmzp69xrTz//PO287H6nEnHvYvXc4XVwx2vbfOcZC7T0KFDjWm/GcU1LVNqPaTur1LV94HkP6DRwenN0+0AagO4B8AyEdlQ8m+jiOi/KgBQSn2hlEqlPZkIIJx3mDHT0eYp044dO9KfU32y6LDHHnvgww8/zHkz0axZcfnVzc1jpUqVPFelq1q1qqv2Qc2aNXPdjggo/WQ488nY4MGDPZ8cdtttN083K9kZ1I4//vj058MOOwwXXnihq+kce+yx6c+ff/453n77bdcxmCazsWy5cuWw1157oUWLFmjTpk3OcTMLXkCwhCpuNWjQABMnTkz//cQTT3guvOZ6yu6mLdCZZ56Jww8/3LJappVcF2Evx1AuDRo08DQ9EcFHH32EN998s9T3mdvXhKQMftm1Ccrs4Nhp/l6qqHq9rqSOP6vkQVEy+Sl8tt69Szf/7tKlC1auXKmtIG/F7foJuh9nVnlNzdOu+pfb/sf8VDnMlKuKuW6XXHKJtuQIQffrzGu9TtnNLuz2myDJQay4SaplMqc2T7sppXZXSlVRSu2d8a+KUspfQxJvrgZg1/BEAfhCRKaJSDeniYhINxGZKiJTTelsVvfFIfM1tpfEApnc3ODYHVT77rsvlFKBnt4FWSd+LhKpJ+6TJk3CmDFj8PHHH6d/y1VwOfTQQ3HOOeeUieHdd99N/51rebJPRNWqVUsn4PDSbqJnz57pC9JJJ50UamrRsG9q/E4/13gPPfSQr+lasXtK61b2vrpixYr0Z799Gtllbcx8kJL5wCKzA8Nsft946tS2bVtcdtllpb579tlnce211wLQV6ferqCbKjyYWj1It9Q+mTrvffbZZ47VZOl/squXly9fPrRqh1E/7bdKthL0mHj99dfTn+3O27mSPCWpcJ0EuZJxhbW+k74d9T1idElExorILIt/7TKG6QtgJ4A3bSZzklLqGADnAugpIrYNVJRSLyilmimlmkWRAcwN3W+evLrrrrtw3nnnleqkNUjj9yDDeJmvzvX06aefAih+45Nd6MtVeKlZs2bOQoqfWJ988kn8/PPPoXUk6/fia+orerdxuXnT2apVK1fT6t69u6vh3BIR3HVXce3kXB2wOk3Dyn333Zeuh+824UTmG1AnfhOxBJFre3s95rIfGKX+vvnmm70FFiGrKmypNlpBr29t2rSBUgqHH354qarWVJjCOO9b3ahntq0CzMrEmPQb/CCyl93U+4C4RF54UkqdpZQ6wuLfRwAgIlcCaAPgcmWz5yqllpX8vwrAhwDcXfELVM2aNdG0adP037Vq1cInn3wSageSYR5o2dNO3VC4mWeqzYmfRvFhyHwLUL58+TIJNbzIPFwyC8ZJ4fbG3S+7C2HlypUxcuTIUm+BorTvvvsCcH4C6PV4cvOApkmTJpaNxN3MK4ouAOLit8pV2DdaHTt2tKwhkHqL7VR7wG9j9tQyua2WpYPbaoN26f69dvxrt7/n283i1Vdf7Wu8sPfrfFvPSedme3CbxVB4ciIirVHch1RbpdRmm2H2FJEqqc8AzgYwK7oog4vqzdOHH34IoLhq0A8//FDmdzcNQpNg+PDhuPfee11l2vvpp5+wePFibfO223ZuC2dueqLPJay3hlG74IILQknN72b9VK5c2TLlfr7I3t7bt2/H9OnTywyXhPNA5rLE0cYvs5CfhPUFBI8zaDs4L/Pv1auXq+Hs2oDMnz/f9by8Ssr2zjRrVvHt0UsvvRRzJKWZeA1KCdo2K0n8JANJ4nGgm1GFJwADAVQBMKYkDflgABCR2iLyackwNQF8KyIzAEwG8IlS6vN4wvUnqpPGBRdc4HrYXOl5wxZkndStWxf3339/zgP6qaeeQtWqVV1Viwuaxj3styiZhgwZggMPPNCxDwk7pp0EvWQTyxe6zgde3gBXqFAhcW16MpclVW0wjp7u33rrrVCnr+uYzNyvwsz+ptNZZ50VuKAWZrIG3Uw7/9rRtW/omE5Y++no0aMdu3bJJUgzhjCvAUGnEWWbJ6uH/KYyqvCklDpEKVW3JAX5UUqp7iXfL1NKnVfy+VelVNOSf39XSvnvBjwmmTtN+/bttbej8OOYY46x/S2VdSezjxpdorx4BOmh3WSdOnXCokWLLG8ahg4divPOO6/M91HeKPlty1NodB4LQdIg62TCDblumU+lwzh/hTHNzJT6UcyvUOX7ujS13WzQ84yI4Oyzzw7UtYsfujuUHTt2LAYMGKB1milR7NuZzUtMZ1ThqVCcfPLJ6c+DBw/29cYgSi1atED//v3x4osvlvpe541RPt5k6RTWRSuKi9rw4cNDnUdYwujtPcq2gH7G0RWf3+nwPGAvyLp57rnnNEbinde3YIUgrOyiSeG3k9wky15W3enHK1SooK1ftNT2yZf9Tbdk1dvIE6NGjSqTUSbuHdTpBCYiuPHGG32NG2S+pJefdf3OO+/Evm/qki/L4cY+++xT6m/Tlz2urJtRTDdTkPOd3yo/1apVQ6VKlTxPl8qK83oV17yZMKKwFEoClaD45ikGe++9d+C0skRuBbn45UN/L/lw0ve6DNn9JHmZj6k301Ftx+zUyUkRdP3kw3ESt6TtM05M3R+Suo6TGrcVU/eNKLHwFJN8OpCcFMpyJkHmCY/bxRy6t0X79u19N7qPo/8mXUxqdG0Cr+vDaviwzhMmrWOTYjFVGPuBn2nyuhUdVttzxsJTzPL1xG3KcpnScD5qbpc76cuZT0zYFqa3vyTvdLR7jGrf9Hu+/vbbbzVHEo2k3ZiGmSAlaetCJ5OWndX23GHhibQJcgLQffLggV7M5GpYFL8kHieFuj8XwnL7Xcaoujjg8aJf0A7AkyyM/cmktPL5jIUn8s3u4PJyQkjixSipuK4LQ5TVr6JUKPtvPt5MFsq2M0nQdR72fpVv2QZNjcsr098ym4KFJwpMx8GW1AMpqXEXonzfVk7HYb4vuw6mrCOT+tIxZZ1QfJhtr7BZbX9uMxaeYmPaRSmugyGpB2FS47aTb8uTKR+WzaQbai9MO8+5Efc60yVIwgh2lkxJSjCko5PcMKabRDpqFBUCFp5iVoj9RvhViCeysHBdmkP3tsj3bZvvy6db3B1le5lf0qtyeYlDKZW4a7DOeLM7yTVlGwLJuzcKA9eBMxaeKHGiOKjz6cThdFHiE2Z7US9LmP30xPU0Uef0TTwmo2yflE/HFlFKPrbxC0OhLrepWHgiI/DEEL4obz5NvNH1Kx+WhcdX8sWxDQthv8mH45vCE8X+ofM4K4Rj1gQsPJFvbg7SXMPwwhUOUzo1NEnS4/cj1/GVT+sk7mWJa/5RzDfp5+m4940kU0qFej1J+r7lVlITLyQ17rCx8ESBWR1IPLiiEUXh1PRtyQJ6aUm7UfR6c5a05XNDR7KG7PGCJIwgCvu6XmiFp6TjdiqNhaeYmHah4oFBSZMv/VG4mb6bZfWzPkw77p3iMSmxQZKYljCCKAjT7p1SkvoQL5/fmIeJhaeYmXpARS3pB5JpMtcn160/+XSzzn0gOfLxmuB2mfzup+XKlfM1HsWH5yRKMhaeKFam3iiYGpdXmcuRpH47olQI64JPF5MvyevSbex+l7FmzZq+xtMtydsoLkGrm1K42ObJGgtPZISknjCTGndK6iRYCCfD7G1l8jLrii0fO79O+jGXS9B1Z/r60blvJPmNk2nbyS6esBJG6GR6fFEKa13YPYgtVCw8kW86DtKkHoRJjTsqJl3MkritTFp/ZAanm1sgnIQRhbAfJvH84IXOtjhu97EgmXhz9VeXj/skU5UnDwtPMcmnHZyv3c0T1zaI8kYkX/azKDsxjnr6YUhizF7oyrbndnphJSMpBCavl6T062fyOgxbIS970q/fLDzFLM6DR/e8raaX9AMk6UQk77dBvlyAgi5HavxCfYOgS5An8SYxeZubvN7ixPViBpOPHTIDC08EIJqCFMWr0LZJPi2vl7cD+V54yqdl8cLvcifpOCi0bZukbaNbErZ1GP1aJU1YcSd1faSw8ERkId8vakk/cfmRvcxhrAPT9hvT4rHiNka+2XankNZJEvbvfBDmPuV1G0a5fxfSsZQLE0aUxsJTATPpxGBSLIWkEE6C7CQ0/3CbuRdkXek6L3N7+WPCesvcB+ziybfrt5/1bso6MCWOfMfCU0zyYQe3y/LkhVNVI7v55aN8XrZC4mc75vO2V0oZcQMYljCWzcQn8dyG7uTTsRx2IhGd6yqf908yEwtPMUvyQa+j8ORHktdZtnxaFgomzH2hqKgotGlbYT9P0TP5XOLm7QWZy+sxly9JV6IS5zkt1/rn+dYaC09EeY4nv8JidaOaD/tAVElt4r6ZM6lqVNB5hv0WTce2imt7e61tYcIxHKR9Yja/y2PCetAl7nONlxhMiNUkLDwRgPw9MMI+0Zp6IreKy2kb5+qYkOLFvnhIR6e3fueps++pMM41hXSOiuo4j6rgpmN5woyvkPYtt3itMbDwJCL/EJHfReSHkn/n2QzXWkTmicgCEekTdZzkTlI7a82XJAM640zKMtvhRdCdpG9nt3TvD6buX362Z1L2gaTEmWSscklUVvm4A7DxtFLqn3Y/ikg5AIMAtAKwFMAUERmplPopqgDJGV8Fm83UG70wmL6PFdK20CHf11ccb5jIn3xe92GfN/N53cUpKes1KXHaMe7Nk0vHA1iglPpVKbUdwDsA2sUckydJ33GA/FiGQuflApm07Z2UeKNIvJKUdeHE9EJwXLK3bb6tp3xbnkLkdP4xMbtkpkLf//Lh2hEGUwtPvURkpogMFZF9LH6vA2BJxt9LS74rQ0S6ichUEZm6evXqMGINxNQD88gjj3Q9rKnLQMWibvRtmqTEryvOfE0YYSKT9i2TYomKacsc9DgL4zjVERP7+8ot1zpy+j0J5+d83nZ+xFJ4EpGxIjLL4l87AM8DOBjAUQCWA/iX1SQsvrPc+5RSLyilmimlmtWoUUPbMuhm2o5ZuXLluEMgzUzbx0gvE7dvEqugxb0edWXbM2ldul2nJsTsJKqMj1FO226du0n2YUo/T0kV97mG/IulzZNS6iw3w4nIEAAfW/y0FEDdjL8PALBMQ2gFiwcx5bskX6yTUr3Sqgqim3iSsnxOdJ1DTbpB17Wus9+GmrQNee0rFleKfK7/5OE2M7Danojsn/FnewCzLAabAqChiDQQkYoAOgEYGUV8VBh4ckgWnXXqoxZFlRgT2gmEnQGTStO5zXWmKg+DSYUxt0xPyx718ea236skbuskC3N9V61aNbRph824whOAJ0TkRxGZCeB0ALcCgIjUFpFPAUAptRNALwCjAcwB8K5SanZcAfth+gnAawd+fsYHwnvibPr6DVuQ+tdxMjWuKCQpYUQhb6e42K3zJCSMCGt/MXFZw5JvyxpFopygdO+3USxrWOf6zNh1LUfr1q21TCcOxqUqV0p1sfl+GYDzMv7+FMCnUcUVFpNPHG5FvQxe5mf6+o2iSoSbtzKmrSfT4kkqrsfCYeK2Nj2TGrlTaJ3kmngshSWMB92FwMQ3T5QH/n979x4fRXX3D/zzTQgJJAhR7heRW4BguAlIRLwABUHkIoLijfYRBYoiVlsEBVGqRflVRCkg+FikLSIVrFcu4rWImIINCCgUrI8VtWi1Slu1Cuf3x87GTbKbnd2dmXNm5vN+vfaV7Ozsme/MnDk7Z+bMOWE50PzUC1FY9gmZyY3eBMPEjx1GkH+50fSWKMrv5RMrT0Rp4I+Iu5woWKvuI78X1mHn1TEXtGPbxPXR1TmBSUzcL/G4tU9Mf+bLS053quM2k2LRhZUn8g0vC00WDnq4sd2DsC8zXYcgnnAESRDyqE6m5G9T4gDMiqUmQcn7ftneqQrqemWKlScC4K8CzE+xEiXjx84ceAy6x6Rta/qJk91BoE3apl4L87qbJNMeYXUfi7ywWRkrT5qY3tNMpr3tkTnCsJ9M6qY7XUHsytuNbW/S/kx1m7uxj0yuLLv93IwTaZl43JjGlOOY+yo5k8rHIGPlSRPTK0+pCMI6hEGqA5cGTVjyqQn7luM8pS/d54FMfsbPpFgofSbcfWBeIhOw8kQUQOx+1L6g/BhzXwZbuvlUZ77gILmJKaV8Oe4PhUM038TLP9F8+4tf/AJ79uzxNC5TsPJEcTlV4LLgdleyH1+eUH/PD4OJOo3HX+rSzRfc1onZ3TapbsMwHMPpMnFogNi0WrVqBQAoKChwLH3KXKJ8E2/6Kaecgi5duqS1HL+Xl8YNkkvB4PcfNdPi92Iw3aAyfd381k2tbn7/0Q0T5lv/ymTfVf1uvLSWL1+OUaNGoXv37mkvJyjCWKZ5defVLbzzpIlpB4vJ7eWJwsDPPyR28Jkne0xYT5b/6QnqdnNjverVq4eLL7445e/169ev2jS/bncTjnWd/Lz+rDxp5ufM49cCK2zYBDNcTNhPJsQQNIm2aarTTebHmMPEhPOVZcuWYffu3brDMJaXx1CYj1dWnihjJhSolFy6ve1x//qDCfvJhBjCKpVnFeKJLRPsjp9kikziZJ6Nz9R9n5ubi5KSEt1hhEZNHUaEGStPRAHE3vaqM/VkwAu61t2t5YZ5XzrNThlgajlhalxBYtKx5lQsxcXFuOOOOzxbntNp6cBjrTJWnijQ/F5gZYoFnvnbIIh51Kttbvq+TUeQ1ineHawgDGgdBkEeF3Dv3r2YPXt2ws9j1z1Ix6Np/LxtWXkyhGmFk2nxpMrPB6UT/L7/wqimPBuE/ByEdajKy3Xy4zEdxH3uFO7PYPFy2/Tu3duzZVF8rDxp4seCM5ni4mIAwMyZMyumBXE9/URE4u4Dt/cL9zuFgQknk0HqMCJdYVpXCp5U829ZWZlLkVQXG1unTp0SfhY2rDxpZsKPL5BeHFUPnPr160MphREjRriyXmE+UJ3YnvHScHo/mZKfw0xHZdlLQVqXeBIdQ8nWO9MOI9I1derUpPO4tc/81rEFJRem3xATzpPsxiAi2LFjB44cOeLIcef3Y5aVJ8qY14VdmApXt1QtuMKwTf1eWCcT9BPJMORRP1q8eHHCz9zeZ6mk/9JLL2Hnzp0uRhPMYy+I60Tpyc/PR6NGjVgWg5UnolALciHol3Vz6uTE9PU1PT6qzusTZzeXd84556Bnz5549dVXXVtGqkw+JvzUYYTp8VF8Juf/ZFh5IiIygJ/rU99KAAAgAElEQVR/SCjYgtS8t3///pXeB/24c7JiEfRt5Vf16tXTHULosPKkielXSkyPj+zjviRd3Mh7zM/2hGk7ebWuJ5xwgifLcQIrOuGwe/dudOvWzbX0azq2wlTGVMXKk2Ys4PzNT4UH81pq6tSpozuECpnuO6/zqRt5Lez5N91e9Uzbbk6O8+T1upWWlmLNmjVo2bKlp8vVwU+/bUFy7NixlOYvKSmp9N7tzlmq/h9WrDwRgOoHg50DMMyFq+mFR5j3TSKpbpMNGzbg9ttvD8WJUqxatWq5kq7px4wp0t1OJm7fIHZicvHFF6Nu3bq6w9Bm27ZtuO666xxLL5pH8vLyHEvTRHaPgawsnpb7AfcSZYwjxpvLxBMqr6W7Ddq1a4c5c+a4vg1NO0ZGjx6Nm266SXcYCZm2vaLc7vjD1PWuCcufxPyyP5VSlWItLS1Fx44dHUu/UaNGmDdvHrZs2ZJwntGjRzu2PDKHn8sHVp7IFX4+KMLALz/cYeLUMZPpvq1VqxYWLFjgSCxOYpkSfH4vl/wefywvxgWMpnnrrbeiQ4cOcT8/cOAAbrvtNseXS/a4laf9fqyw8kRx+T1j0/dq2pc8IQ2OdPZlz549XYgkGOyUgSYfP06U4dEmnJn25uX27wl/r4jcFa+sC/Nxx8qTJmHOdKSHySd65K5E5c3TTz+NrVu3ehxNZddee63W5Vdl6nGS7m9GJutTUlKC+fPnY82aNZ4v26v0Td3ffhaUbRp7zE2ePFljJGYJyv7NBCtPmjETkgkyzYetW7d2KBKKJ5X9k+wkO/bzBg0aoF+/fmnHlYp46/DQQw+hsLAwbmyJOH3hafDgwRg4cKCjaXrJzQtxIoIZM2agWbNmri3DCbwYGTxXXXUVAKBp06aeLzteWXXiiScm/d6yZcvw9NNPuxGSbTwWvGFU5UlEHhORcuv1noiUJ5jvPRF5y5pvh9dxBlGLFi1S/g4P0nCpaX9v27bNw0jSY2p+dbujAT+wE7tb63fXXXfV+LC6X/h5/6crjOvstaodRnhl6tSpUErZajJqJ77p06c7EVaNJk2ahOHDh7u+HL8644wzdIfgGKMqT0qpi5VS3ZVS3QGsA7C+htnPtebt5VF4gRP7w9O0aVM8/vjjGadDwWNn/zZv3tyDSNLjl/zplzhTYWqF1WQm5QM3KvZurJ9f85lJ+7oqrzqM8MrChQt1h+BLTh5bmzdvxn333Vfx3s/5yajKU5REtug4AI/qjiVM/DR6OtUstsCLV/iZdLJRXFzs60LUNCbs23j7s3PnzhoiCa9M8oFTx2Oq4zwlm6/qRRpd5Ubjxo0BADk5OZ4tMzr+j4njAIWt/H722Wexa9cu3WF4yokOI/Lz8wPTxN+8ozCiP4C/K6X+kuBzBWCziOwUkWs8jMsxJpzg1MSp+JxcT9O3mYmSXfE14Ufvrbfewrfffqs7DN+raV/eeOONHkYS34YNGxxJJ+g9t6U7ztNZZ50FIHIxwk56XnC68vT666+ntPyhQ4emNL9d69atw4oVK9C2bdu000g1n40ZMwZTp07Fvffem/YynVZUVIQbbrgBTz31lO5QXHPyyScDqPxc77Bhw9C1a9eU0lFK2Xpuyg9MOG/QzfPKk4hsEZE9cV4jY2Ybj5rvOvVTSvUEMBTAVBE5q4blXSMiO0RkxyeffOLQWjjHpEzYt29fx9Jyc71M2maUuaysLGRnZ+sOI9DOPfdc1K5dW2sMDRs2zOj7Xh/3ppUzyU62f/jDH+Lw4cM4/fTTPYrIe9ETWbvc6smxcePGmDhxYrXpbla8a9eujcWLF6NRo0YpfS/dmOx21X/vvfeiqKgorWX4weWXX46NGzfi6quvzjit/Px87RdndOvfvz8KCgrws5/9zLgyNhWeV56UUoOUUqfGeT0JACJSC8CFAB6rIY0Prb9HADwBoE8N8y5XSvVSSvVKtdAJm3r16qV0KzrshQD5S6L8amI+jlYmo7H5+UeGgI8++giHDx92Je3YPGLys4fkLScH3TaxjHRC/fr1k84jIhgyZIiWMvj3v/99xf9O3b1N1bBhwwBkfvEr6qSTTsLRo0d933mEic32BgF4Ryn1QbwPRSRfROpF/wcwGMAeD+OjKnhiRyZLlD9NybfxfuzKyspw6623Gvl8A6WuadOmrldsTMnP8Zjc1NLk7ea1RYsWVfxvajPvqKeffhrdu3fPKI0///nPeOyxhNfpXXPw4EG89tprSee76KKLMGrUKA8iSuzuu+/G4cOHK57zc5JJ+SlVJv4yX4IqTfZEpLmIPGe9bQJgq4jsAlAG4Fml1EaPYyQiRLp/LSgo0B1GXJs3b8a4ceN8UwGJ/SHp2bMn5s2bV+P848ePx9SpU90OyzhBvQoe5VQLiZNPPhkTJ07U+jyKEydHrVq1cjV9J/Xs2TPpPAMGDEj4WcuWLSu9v+CCCzKOKRXTpk3DbbfdlnE6J510kgPR1Gz48OG4//77M0qjTZs2GDdunEMR2deuXTvf3HnJzs6uduGnQ4cOAIAmTZroCMkIxp1VKKV+qJRaVmXah0qpYdb/7yqlulmvLkqpO/VEmplkJwB169bV2gTD6ROURYsW4bLLLnM0TTtSWY+gnojabbuejoULF+Lo0aNpfffgwYMZjQ/VtWtXtG3bFvfcc0/czwcOHIjHHnvMuBMsp6xevRqLFy/WHYZnTNyPmcS0YMGCuNOd6pUwKysLK1asSPnBdrc1aNDAdqcYjz32GF588UUvwsrY7t27bXXMcumll8adPmLECJx22mkV75VS1Tr/cJobx9SSJUvwy1/+suL92LFjHV9G0PXu3duVO07xLkSkkwdmzZqFTZs2YciQIU6EBQC+e1bTuMpT2CTKuAcOHIjbRn737t1YsWKFozF06tSpWjxOV56mTZuG3/72t46mWZN0CoTFixcnXe8xY8bgwQcfTDcsz8Rbj9htYsLV+3bt2qG0tDTt7+fn5+PQoUPo37+/g1E5z+vOMEzYt1EmxWKS4cOH46abbkrpO0HZli+//LLteceNG6f9OS67lc+SkpKM7nJn2gQtVY8//jj279+PgoICjB8/Hs8991zCee3kvbKyMjz88MOYMmUK8vPzAUTy+dq1ax2LuapmzZoBAM4880zXlqFDWVkZnnjiCcfTff/99xNW3lORnZ2NwYMHOxBRxMMPP4zt27c7lp4XaukOgFJTUlKCkpIS19I38equSdIdSFiXZPszTPs7KCefibh14SMdXuarESNGBLar5A4dOmDgwIFYtmxZwnlM2N/JxOaHtm3b4tixY46m79Y2eO+99wLTvXRVY8aMqfh/9erVtr5T03Hdu3dv9O7dO+O4UtG+fXu88847aN++PebPn+/pssk5fijDquKdJ0pbYWEhgEgvfYn48aCgYDG9guhUs0rT19MpVbfXk08+aet7a9asQXl5uRshAXCnrNu+fTsuvvhiW+mbuP/Xr1+P888/Hw0aNHAlfbfXuXXr1jX+vmXKxH1Wlem/4R07dnTt7n6ydX/hhReMe6bW9P0Vyw/5PxHeeTKU3QOgS5cuLkeS2OzZs9G0adO4zzKZdFB069YNn332WcbpzJw504FoiOIz6ZhJ1913340jR4640pQy0+0TrYQ4yY19VjVNP+eLc845B+ecc47ry/HTCWOUUiqjZstu83O+qyrVZn12133AgAE4/fTTUx68mfzPrCozpeTrr7929UpqMnl5ebjuuuscvfJy4MABbNq0ybH0AKC8vBzvv/9+xulk0rPMU089FXdQRS9cfvnlACLPGFHmLrroIt0hGKtr167YuXOnsT0wxoo+S5OXl6c5ksTSqRSccMIJAFCtudnOnTvxt7/9rdr8bdu2BeBcL3+ZSLS+R44cwT/+8Y9q0504wdc5+HJ2djbmzp3r6fLD6KGHHnJ9GcmOVR29+qVi4sSJeOedd3SH4Ru886SJE1fKcnNzHYgkPl1X8jp06FDRDWaQXHDBBbjgggs8KcSrmjRpEq655hrjmhf41dq1a3H8+HFty8/NzQ11F7FOWbVqFZ5++mmtd+9TYbdMHjt2LD777DP86Ec/qjQ9UTfac+fORWlpKQYNGpR2bEuWLEHHjh3T/n5UonU0oWIXNrH7ItpphtedWjildu3arqVtp/LthzujjRs3RseOHbFnD4dNtYOVJ81MuzWuK54f/OAHWpYbBiJSbb/6oTA3lYh43oNerK1bt6JXr17alq9LmzZtAETGL3JCYWEhrrzySkfSinXPPfckHMMlegEjlfyTapmclZWFKVOm2J4/JycHw4cPT2kZVaWyPDckK89MLu+i3ZE72S15JhdW4+W3ESNG4MCBA769sOlUmWGSffv24ciRIwC+v9tcq1bqp/RV80r0IouXQ8uYfHwmwkvRpN3hw4fj9pal8wQ1LKI/lLHjiwSVrgI62clvnz59AMBWhWjhwoVJ5/PTD1G00xk7rr76amzevNmRrnbd9NOf/hT9+vWL+9mQIUMwbdq0GnvPS1WPHj0AeDMwqWmC0Jvo2LFjUV5eXvFMXl5eXkbds3/33Xf497//XfE+3fIg2uzz+uuvB4CUKk7RMkp3pToqiOcSnTt3xtlnnw0AWLlyJebPn4++ffsm/d4tt9xSaXiaquVpmzZtoJTChRdemHGMs2bNqvFzPxyfifDOk6Hc7OHHNIl+KP75z39qbR7lZ9ErUXavQK5evRpvvfWWb0Y9T4XpBfSIESPwwQcfoEWLFgnn8Vtve9HniEpKSvDCCy8knG/06NG20xSRhHeoZ8+ejREjRnjeVXKqatWqhUWLFtme386J77333osrrrjCkWZzTurWrZvuEIxSr169hAOKx26rdAcdHzp0KDZt2lRRUci0PKhbt27aFa9mzZr56iKO3zVq1AgzZswAkLzMKC4uxttvv12RP9K5W2VH0Pc/7zwZZubMmThy5Ajq16+vOxTtCgoKKioBAPDggw/iBz/4AX+UkfyHceHChZg/fz7OP/98W+kVFBQk7Plp8ODB6NGjB26//faU4wybdDsfqKni5IVu3bph3rx5Sefr2LEjWrZsmXS+wsJCvPzyy0nHRXOqwnfHHXdoacrodoXVzglIbm6urSvOXnrvvffwxz/+UXcYRvnoo4/wxRdfJJ2vVq1aaZ3QPvfcc46Pn5XI0KFDAQATJkzwZHlEpuGdJ00S/ShmZ2cb8XCsU1cNnLz60KNHD2zevNmx9LzQvn17LcutX79+xZUoJ9J68803HUkr6NauXevL9vV2e+2M9sZk5wJGtEmJX7Rp0wZ//etfdYeBunXrVnpv0uDHdrVu3dr2vJMnT0b//v1x2WWXVaxjUVFRwruMa9asqXaRouq2ee2119CkSRMcOHAgxcjdk5+frzsEx7Rt29ZX+ZHc98orr4SqUypWnjQzqakN4Fw8pq2XDu+//z7vIIZMq1atdIdQSatWrVBeXo46deqk9f0wHcevv/66EV313nzzzbjjjjsq3p9xxhmYMmUKfvazn2mMyh1ZWVlYunQpdu/eXWn6/v37E34ndryuRPkz2vzYTuXp3HPPtRNq2lauXBn3mVJWPtwzc+ZMTJgwodKzPVTZ888/j5deesmx9M4666y0v+vHYyE81USiFA0YMCCj77dq1apSs0OT+LGwypRX63zqqadWeu/Eg7epiF3PVatW4dFHH03rJOK+++7Drl27apxn6dKlePfdd1NO20RNmjQx4m5ZnTp10LBhQwCRfZmdnY0lS5bglFNO8TyWoJcTBw8exIIFC1xdxoQJEyqVCW5ckDDtoo1u/fv3R8eOHbF+/XosX75cdzha3HDDDTV+PmjQINx5552exLJmzRr8+te/rjbdzxfneOeJKIFTTz0VL774ou4wXJVu4TV58uS4J9bRJqeZVjyd5HUB/eqrr+K9995Dr169cPz4caxcuRJr1651JO2aTmbjrWeDBg1wySWXpLWsaC9bNSktLa3oQtwv7rzzTqxatUp3GDXSfVKhe/leCcLA4V988QVycnJ0h2GkVDqkccukSZN0h6Bd7N3ioGDliYhStnTp0rjTW7ZsiUOHDml97kd397iFhYWVuuB2YoDGVE5mTbtb0KxZMxw+fFh3GBVmzZqVtAtdr4XpWQFyViqtG4YMGZLRGFCUGh1lcbS3Rbd60aMIlthEPmTyleG2bdtqK7j79u2LJUuWaFm2bqbmiddeew2rVq0K5FgrThkzZozuEHwpeqHE1ObRptm4cSOefPLJGueJdsaRbs+hNaXJMsB9kyZNwvTp0zF79mzdoQQaK0+amHZ1OKqoqAgjR47Eb37zG92hJGW3G25yzsKFC/HGG29ojaFu3bqu91wVO8hkuiZPngzA3EqNV1q3bo0rrrhCdxhGc+LupJ9E77RFT/Ciz3P99Kc/TSmdOXPm4NixY9V6KAyLgQMHOp7m9ddfjzlz5iR9ZiYVS5cuxcyZMzFkyJCk8/72t7/F+PHjUVJS4tjyUzVnzhwA1XtInDVrltZmyr/73e/w6KOP1jhPXl4eFi5c6IuxQqPNKk8//XTNkaSOlSfNnDqxGjZsmCPp1KpVC3/4wx/i9g5kmnXr1uHDDz/UHUYgNW7cOO706dOno0+fPh5HU9nnn3+Ozz77rNK0Pn364Mc//jFWr15dbf4mTZoASO35hqonY3//+9/x/vvvpxTnAw88gK+++opNsjy0d+9e/OlPf9IdRsaid1T8cAKUDhGBUgpz584FELlzpJRKuZItIqE9vpRS2LJli+Pp5uXl4fbbb3f0zlPDhg1x1113ITs7G0899RS2bduWcN4uXbpg9erVWpud3XDDDVBKVbuoceedd9rqIMetweYvvfTStJ9hTSTaoZHT6dpx/vnn4/jx41oryulio8iAWL9+Pb788ksAwJtvvomPP/5Yc0QRbt5hy83NRbNmzVxLP8z27t2LTz/9VHcYccW7Sp+dnY1f/epXcecfMGAANmzYkFHvQokqkzXJyspy9ASEkisuLta27C5dujiW1ty5czFnzhwjmznVrVsX//nPf3SHQT51wQUX6A7BdZs3bzb297Oqzp07a20J5deWGeG8ZBNAubm5FT2d9ejRo2IEcF38ekCERbLCsmHDhoEaI+O8887jA7TkKifHYRIR4ypO06ZNAxAZg+mVV17RHI1ZotuGCIg097MzUHRRURF69+6dsAOmTM2ePRv9+/d3Je2w49kEkQ/l5+fj6NGjGafDSq6/mPqsZFhFj5+SkpK4zcdycnLw7bffeh2WKxYtWoRFixYBiPSqSRGpHpNjx47FunXrKg2E7IYRI0agvLwcTZs2dXU5lL7c3FyUlZW5lr7beSzMeOdJk+gzRTx5pUTWrl1bbXygiRMn4oEHHsB9990HQG8zJTfUr18fANCzZ0/NkZjFtK7K77jjDmRlZQVinBw37d+/Hxs2bNAdBtnkRWcABQUFeOaZZ1wf2Pa2227DkSNHQtO0nWUReYl3njTZuHEj3nnnHU+bZrz44osVTfuCrn379gCAq666SnMklV155ZV45plnbM07duzYatPq1q2La6+9FkopnHfeeWjevLnTIbpu6NChCU8oTz75ZGzfvh3dunXzOCr/8/JCzMiRI3Hs2LG0v9+uXTscOnSo4n20HAxar3Nt2rQxbhDhgoKC0Ddh/cMf/oAWLVpUmrZ161Z06NBBU0TOy8rKCs3vPQAcPHgQQ4cOxcaNG3WHQiEQ7hJUo8LCQpSWlnq6zHPPPdfT5enUpEkTI5s4PfLII46kIyK+rDgBwHPPPVfj537stjSZ8vJy7Ny505NlmZjvq9q1axe++uqrivdDhgzBjBkzcOONN2qMKhyq9lSZCZ2DYWdi5MiR1ab169dPQyRE5EesPBFpFO2i07Q7ZOSsbt26uX43zU9NgPPz8yuNoZKdnY358+drjCi+3NxcdO7cudr0Bx980JH9eeaZZ2Lr1q0Zp5OKnJwcR9L5z3/+Y1ynFhRus2fPxvbt29G3b1/doVDAsfJElMTVV1+NRx55BKNGjXI87ebNm2u5U+CHuxNEun399ddxp19zzTUAgN27d2eU/oYNG3w7Vl2dOnV0h5BUdIiBoD0bSvGdccYZ+Pzzz3WHQSHAyhO5orS0FAcPHgzEII9dunTBN998ozsMV/jpbgWx0hs0BQUFKCoqAgDcf//9KC8v1xyRe5o1a4Y5c+Z4uszTTjsNr7zyiudN5MlZ0Wf0CgoKNEfiveg5VNCeB/U7Vp4MEW2+1bVrV82ROGP58uX4yU9+wm5SiVIwcODAuF1em9bbXrrWr1+Pf/3rX54u8+OPP054B8kk1113ne4QXKXrDttZZ52lZbnknOLiYsyfPx9XXHGF7lA8d//996NTp07ax+6kylh5MsSoUaOwb9++uO3r/SgvLw/du3fXHQaRr2zZsiXt7/rhLuLo0aM9X2aTJk08XyYROUdEMGPGDN1haNGgQQPMmjVLdxhUhZZxnkRkrIjsFZHjItKrymczReSgiOwXkSEJvt9GRN4Qkb+IyGMiEoj7mUGpOBERhUH0LiGb1BCZb968eRgzZozuMCgAdA2SuwfAhQBejZ0oIsUALgHQBcB5AJaISLzufO4GsFAp1QHA5wDYVZnPvPvuu/j44491h0FUyaBBg3DTTTfpDsMV0edNwj7Gj5O6dOmCW265BY8//rjuUIgoiVtvvZXHKjlCS+VJKfW2Ump/nI9GAlijlPpGKfVXAAcB9ImdQSJtUwYAiB4BjwBwvhs0clWbNm3YnIaM8/zzz2PBggW6w3DFLbfcAqUUu5d2kIjg5z//OU455RTdoRARkUd03XlKpAWAv8W8/8CaFuskAP9USn1XwzwVROQaEdkhIjs++eQTR4Ml8sr48ePRpUsXTJ8+3ZH0ioqKULduXcybN8+R9MhdI0aMABCuga5Jn5EjR6JevXr48Y9/rDsUIiLjuNZ+Q0S2AIjX1dotSqknE30tzrSqXUfZmef7D5RaDmA5APTq1cvcbqiIatC4cWPs2bPHsfQKCgrw73//27H0yF1nn3220b3oUbA0b94cX375pe4wfC0vL493eYkCyrXKk1JqUBpf+wBAq5j3LQFU7d/0UwANRKSWdfcp3jxElIbS0lIcOHBAdxiuKSsrQ35+vu4wXJGXlwcAaNiwoeZIiOiLL77QHQIRucS0ZntPAbhERHJFpA2ADgDKYmdQkcuvLwG4yJo0AUCiO1lEaevUqROAyPNZVV1//fXIysrCgAEDvA7LVdu2bcOnn36qOwzX9O7dG8XFxbrDcEWPHj2wbNkyrFy5UncoRKFXu3Zt9sJIFFBaul0SkdEAHgDQCMCzIlKulBqilNorImsB7APwHYCpSqlj1neeAzBRKfUhgBkA1ojIzwH8GcD/6lgPCrbJkyejW7duOOOMM6p91qdPHxw7dkxDVETxiQgmTZqkOwwiIqJA01J5Uko9AeCJBJ/dCeDOONOHxfz/Lqr0wkepa9o08kjahAkTNEdiJhGJW3EiIr1WrFiBGTNmoKCgQHcoREQUMhzwI8QKCwvx3//+l+O+UChFBzjt04fXYfxm3LhxGDdunO4wiIgohHjWHHI5OTm6QyDSIicnB2VlZSgqKso4rdzcXAciIiIiItOx8kREodW7d++M09i3bx9OPPFEB6IhIiIi07HyRES+cNVVV2Ho0KG6w6imc+fOukMgIiIij7DyRES+8NBDD+kOgYiIqEKjRo0AsOl22LDyRERERESUol//+tdYs2YNTjvtNN2hkIdYeSIiIiIiSlFhYSGmTJmiOwzyWJbuAIiIiIiIiPyAlSciIiIiIiIbWHkiIiIiIiKygZUnIiKiKrKzs3WHQEREBmKHEUQOKisrw5YtW3SHQURpysnJwezZs3HhhRfqDoWIiAwkSindMXimV69easeOHbrDICIiIiIiQ4nITqVUr3ifsdkeERERERGRDaw8ERERERER2cDKExERERERkQ2sPBEREREREdnAyhMREREREZENrDwRERERERHZwMoTERERERGRDaw8ERERERER2cDKExERERERkQ2sPBEREREREdnAyhMREREREZENrDwRERERERHZwMoTERERERGRDaKU0h2DZ0TkEwD/pzsOS0MAn+oOgigFzLPkN8yz5CfMr+Q3Qc6zrZVSjeJ9EKrKk0lEZIdSqpfuOIjsYp4lv2GeJT9hfiW/CWueZbM9IiIiIiIiG1h5IiIiIiIisoGVJ32W6w6AKEXMs+Q3zLPkJ8yv5DehzLN85omIiIiIiMgG3nkiIiIiIiKygZUnDUTkPBHZLyIHReRm3fFQuIjIeyLyloiUi8gOa9qJIvK8iPzF+ltoTRcRud/Kq7tFpGdMOhOs+f8iIhNipp9mpX/Q+q54v5bkZyLysIgcEZE9MdNcz6OJlkGUTII8O1dEDltlbbmIDIv5bKaV//aLyJCY6XHPD0SkjYi8YeXNx0SktjU913p/0Pr8FG/WmPxMRFqJyEsi8raI7BWR663pLGdtYOXJYyKSDeBXAIYCKAYwXkSK9UZFIXSuUqp7TBejNwN4QSnVAcAL1nsgkk87WK9rACwFIoUfgNsAnA6gD4DbYgrApda80e+d5/7qUMCsRPV840UeTbQMomRWIn5Zt9Aqa7srpZ4DAOs3/xIAXazvLBGR7CTnB3dbaXUA8DmAq6zpVwH4XCnVHsBCaz6iZL4DcKNSqjOAvgCmWnmN5awNrDx5rw+Ag0qpd5VS/wWwBsBIzTERjQTwiPX/IwBGxUxfpSK2A2ggIs0ADAHwvFLqM6XU5wCeB3Ce9dkJSqnXVeSBylUxaRHZopR6FcBnVSZ7kUcTLYOoRgnybCIjAaxRSn2jlPorgIOInBvEPT+wrtgPAPC49f2q+T+aZx8HMJB3+ykZpdRHSqk3rf+PAngbQAuwnLWFlSfvtQDwt5j3H1jTiLyiAGwWkZ0ico01rYlS6hMjPXYAAAW3SURBVCMgUqgCaGxNT5Rfa5r+QZzpRJnyIo8mWgZRuq61mjk9HHNFPtU8exKAfyqlvqsyvVJa1udfWPMT2WI19ewB4A2wnLWFlSfvxbsixC4PyUv9lFI9EbkNP1VEzqph3kT5NdXpRG5hHiVTLQXQDkB3AB8B+KU13ck8y/xMaRORAgDrAExXSn1Z06xxpoW2nGXlyXsfAGgV874lgA81xUIhpJT60Pp7BMATiDQV+bt1mx3W3yPW7Inya03TW8aZTpQpL/JoomUQpUwp9Xel1DGl1HEAKxApa4HU8+yniDSTqlVleqW0rM/rw37zQQoxEclBpOL0O6XUemsyy1kbWHny3p8AdLB6zqmNyEOjT2mOiUJCRPJFpF70fwCDAexBJA9Ge8mZAOBJ6/+nAFxp9bTTF8AX1m32TQAGi0ih1RRlMIBN1mdHRaSv1e7+ypi0iDLhRR5NtAyilEVPEC2jESlrgUg+u8TqKa8NIg/TlyHB+YH1zMhLAC6yvl81/0fz7EUAXlQcwJOSsMq+/wXwtlLq3piPWM7aoZTiy+MXgGEADgA4BOAW3fHwFZ4XgLYAdlmvvdH8h0gb+RcA/MX6e6I1XRDp/ekQgLcA9IpJ638QedD5IIAfxUzvhchJwiEAi2ENxs0XX3ZfAB5FpJnTt4hcwbzKizyaaBl88ZXslSDP/sbKk7sROWFsFjP/LVb+2w9gaMz0uOcHVtldZuXl3wPItabnWe8PWp+31b0t+DL/BeBMRJrR7QZQbr2GsZy194quCBEREREREdWAzfaIiIiIiIhsYOWJiIiIiIjIBlaeiIiIiIiIbGDliYiIiIiIyAZWnoiIiIiIiGxg5YmIiBwjIieJSLn1+lhEDse83+bC8nqJyP0pfmdWlfeOxxWT9ikicqlb6RMRkbfYVTkREblCROYC+JdS6v/pjiWWiPxLKVXg0bLOAXCTUmq4F8sjIiJ38c4TERF5QkT+Zf09R0ReEZG1InJAROaLyGUiUiYib4lIO2u+RiKyTkT+ZL36xUnzHBF5xvp/rog8LCIvi8i7IjItzvzzAdSx7oT9zsm4ROTsmLtsfxaRegDmA+hvTbtBRLJFZIH1vd0iMilm2a+KyBMisk9ElolIljX/ShHZY8Vwgwu7hoiIbKqlOwAiIgqlbgA6A/gMwLsAHlJK9RGR6wFcB2A6gEUAFiqltorIyQA2Wd+pSScA5wKoB2C/iCxVSn0b/VApdbOIXKuU6u5CXDcBmKqUek1ECgB8DeBmxNx5EpFrAHyhlOotIrkAXhORzday+wAoBvB/ADYCuBDAXwG0UEqdan2/QZL1JyIiF7HyREREOvxJKfURAIjIIQDRCsRbiFR+AGAQgGIRiX7nBBGpp5Q6WkO6zyqlvgHwjYgcAdAEwAdexAXgNQD3Wne01iulPoiZJ2owgK4icpH1vj6ADgD+C6BMKfWutexHAZwJ4AUAbUXkAQDPxsRDREQasPJEREQ6fBPz//GY98fx/W9TFoBSpdRXaaZ7DKn/zmUS13wReRbAMADbRWRQnPQFwHVKqU2VJkaejar6ELJSSn0uIt0ADAEwFcA4AP+T2ioREZFT+MwTERGZajOAa6NvRCRRU7tUfSsiORl8P25cItJOKfWWUupuADsQaUJ4FJEmhFGbAEyJLl9EikQk3/qsj4i0EZEsABcD2CoiDQFkKaXWAZgNoGcGcRMRUYZYeSIiIlNNA9DL6lhhH4DJDqW7HMDuaIcRDsY13erYYReArwBsALAbwHcissvq7OEhAPsAvCkiewA8iO/vaL2OSAcTexB51ukJAC0AvCwi5QBWApiZZsxEROQAdlVORESkGbs0JyLyB955IiIiIiIisoF3noiIiIiIiGzgnSciIiIiIiIbWHkiIiIiIiKygZUnIiIiIiIiG1h5IiIiIiIisoGVJyIiIiIiIhtYeSIiIiIiIrLh/wMMDnupEFJb+wAAAABJRU5ErkJggg==n”, “text/plain”: [

“<Figure size 1008x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plt.figure(figsize=(14, 6))n”, “plt.plot(unit.Source, ‘k’)n”, “plt.ylabel(‘No Unit’)n”, “plt.xlabel(‘Time in timesteps’)n”, “_ = plt.title(‘Source Signal’)”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}, {

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

“#### Peaksn”, “n”, “We can easily visualize the detected peaks in the source signal as a cutout overlay”

]

}, {

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

{

“data”: {

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n”, “text/plain”: [

“<Figure size 576x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“cutouts = unit.get_peaks_cutouts()n”, “plt.figure(figsize=(8,6))n”, “_ = plt.plot(np.transpose(cutouts*1e-3), color=’k’, alpha=0.2)n”, “plt.xlabel(‘Samples’)n”, “plt.ylabel(‘No Unit’)n”, “plt.title(‘Spike Cutout Overlay’)n”, “plt.show()”

]

}, {

“cell_type”: “markdown”, “metadata”: {

“pycharm”: {}

}, “source”: [

“<a href=’#Top’>Back to index</a>”

]

}

], “metadata”: {

“kernelspec”: {

“display_name”: “Python 3”, “language”: “python”, “name”: “python3”

}, “language_info”: {

“codemirror_mode”: {

“name”: “ipython”, “version”: 3

}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.7.0”

}

}, “nbformat”: 4, “nbformat_minor”: 1

}

{

“cells”: [

{

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

“# McsPyDataTools Tutorial for files from MCS Headstages with IMU<a id=’Top’></a>n”, “n”, “This tutorial shows the handling of IMU data collected from an MCS Headstage wearing an Inertial Measurement Unitn”, “n”, “- <a href=’#Gyrosocope Data’>Gyroscope Data</a>n”, “- <a href=’#Accelerometer Data’>Accelerometer Data</a>n”, “- <a href=’#6DoF-Estimation’>Combined 6-DoF Data</a>n”, “n”, “Load module and the data file:”

]

}, {

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

“# These are the imports of the McsData modulen”, “import McsPy.McsDatan”, “import McsPy.functions_info as fin”, “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”, “import osn”, “n”, “# numpy is numpy …n”, “import numpy as np”

]

}, {

“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”: null, “metadata”: {}, “outputs”: [], “source”: [

“data_folder = r’..\McsPyDataTools\McsPy\tests\TestData’ # adjust this to your local environmentn”, “fi.print_dir_file_info(data_folder)”

]

}, {

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

“Check what’s inside the file:”

]

}, {

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

{

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

“n”, “..\McsPyDataTools\McsPy\tests\TestData\2017-10-11T13-39-47McsRecording_X981_AccGyro.h5n”, “n”, “Date Program Versionn”, “——————- ————————– ————n”, “2017-10-11 13:39:47 Multi Channel Experimenter 2.6.90.17257n”, “n”, “Type Stream # chn”, “—— ——————————————- ——n”, “Analog Data Acquisition (1) Quality Sideband Data1 1n”, “Analog Data Acquisition (1) Electrode Raw Data1 32n”, “Analog Data Acquisition (1) Analog Data1 1n”, “Analog Data Acquisition (1) Digital Data1 1n”, “Analog Data Acquisition (1) Gyroscope Data1 3n”, “Analog Data Acquisition (1) Accelerometer Data1 3n”

]

}

], “source”: [

“acc_gyro_raw_data_file_path = os.path.join(data_folder, “2017-10-11T13-39-47McsRecording_X981_AccGyro.h5”)n”, “fi.print_file_info(acc_gyro_raw_data_file_path)”

]

}, {

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

“Load the file in silent mode:”

]

}, {

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

“McsPy.McsData.VERBOSE = Falsen”, “raw_data = McsPy.McsData.RawData(acc_gyro_raw_data_file_path)”

]

}, {

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

“## Gyroscope Data<a id=’Gyroscope Data’></a>”

]

}, {

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

{

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

“Channel IDs: dict_keys([148, 149, 150])n”

]

}

], “source”: [

“gyro_channel = raw_data.recordings[0].analog_streams[4]n”, “print(‘Channel IDs: %s’ % gyro_channel.channel_infos.keys())”

]

}, {

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

{

“data”: {

“text/plain”: [

“(16400, 3)”

]

}, “execution_count”: 12, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“gyro = np.transpose(gyro_channel.channel_data)n”, “gyro.shape”

]

}, {

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

{

“data”: {

“image/png”: 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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”, “plt.plot(gyro)n”, “#plt.title(‘Signal for Wireless (Simulation) / Raw ADC-Values (%s)’ % analog_stream_0.label)n”, “plt.xlabel(‘Sample Index’)n”, “plt.ylabel(‘Gyroscope Value’)n”, “plt.grid()n”, “n”, “plt.show()”

]

}, {

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

“Remove invalid data parts:”

]

}, {

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

{

“data”: {

“text/plain”: [

“(10000, 3)”

]

}, “execution_count”: 14, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“gyro = gyro[0:10000,0:3]n”, “gyro.shape”

]

}, {

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

{

“data”: {

“image/png”: 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“text/plain”: [

“<Figure size 1440x864 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“time = gyro_channel.get_channel_sample_timestamps(148,0,10000)n”, “gyro_x = gyro_channel.get_channel_in_range(148,0,10000)n”, “gyro_y = gyro_channel.get_channel_in_range(149,0,10000)n”, “gyro_z = gyro_channel.get_channel_in_range(150,0,10000)n”, “plt.figure(figsize=(20,12))n”, “plt.plot(time[0], gyro_x[0])n”, “plt.plot(time[0], gyro_y[0])n”, “plt.plot(time[0], gyro_z[0])n”, “plt.xlabel(‘Time (%s)’ % time[1])n”, “plt.ylabel(‘Angular Speed (%s)’ % gyro_x[1])n”, “plt.title(‘Gyroscope Data’)n”, “plt.show()”

]

}, {

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

“## Accelerometer Data<a id=’Accelerometer Data’></a>”

]

}, {

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

{

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

“Channel IDs: dict_keys([160, 161, 162])n”

]

}

], “source”: [

“acc_channel = raw_data.recordings[0].analog_streams[5]n”, “print(‘Channel IDs: %s’ % acc_channel.channel_infos.keys())”

]

}, {

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

{

“data”: {

“text/plain”: [

“(16400, 3)”

]

}, “execution_count”: 17, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“acc = np.transpose(acc_channel.channel_data)n”, “acc.shape”

]

}, {

“cell_type”: “code”, “execution_count”: 18, “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(acc)n”, “#plt.title(‘Signal for Wireless (Simulation) / Raw ADC-Values (%s)’ % analog_stream_0.label)n”, “plt.xlabel(‘Sample Index’)n”, “plt.ylabel(‘Accelerometer Value’)n”, “plt.grid()n”, “n”, “plt.show()”

]

}, {

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

“Remove invalid data parts:”

]

}, {

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

{

“data”: {

“text/plain”: [

“(10000, 3)”

]

}, “execution_count”: 19, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“acc = acc[0:10000,0:3]n”, “acc.shape”

]

}, {

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

{

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 1440x864 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“time = acc_channel.get_channel_sample_timestamps(160,0,10000)n”, “acc_x = acc_channel.get_channel_in_range(160,0,10000)n”, “acc_y = acc_channel.get_channel_in_range(161,0,10000)n”, “acc_z = acc_channel.get_channel_in_range(162,0,10000)n”, “plt.figure(figsize=(20,12))n”, “plt.plot(time[0], acc_x[0])n”, “plt.plot(time[0], acc_y[0])n”, “plt.plot(time[0], acc_z[0])n”, “plt.xlabel(‘Time (%s)’ % time[1])n”, “plt.ylabel(‘Acceleration (%s)’ % acc_x[1])n”, “plt.title(‘Accelerometer Data’)n”, “plt.show()”

]

}, {

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

“## Combined 6-DoF Data<a id=’6DoF-Estimation’></a>”

]

}, {

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

“import skinematics as skinn”, “from skinematics.imus import IMU_Basen”, “n”, “from scipy import constants # for “g”n”, “n”, “from mpl_toolkits.mplot3d import Axes3Dn”

]

}, {

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

“Load data and separate values from unit:”

]

}, {

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

“time, time_unit = gyro_channel.get_channel_sample_timestamps(148,0,10000)n”, “gyro_x, gyro_x_unit = gyro_channel.get_channel_in_range(148,0,10000)n”, “gyro_y, gyro_y_unit = gyro_channel.get_channel_in_range(149,0,10000)n”, “gyro_z, gyro_z_unit = gyro_channel.get_channel_in_range(150,0,10000)n”, “n”, “time, time_unit = acc_channel.get_channel_sample_timestamps(160,0,10000)n”, “acc_x, acc_x_unit = acc_channel.get_channel_in_range(160,0,10000)n”, “acc_y, acc_y_unit = acc_channel.get_channel_in_range(161,0,10000)n”, “acc_z, acc_z_unit = acc_channel.get_channel_in_range(162,0,10000)”

]

}, {

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

“Define a new class for our data - derived from IMU_Base class in scikit-kinematics:”

]

}, {

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

{

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 432x288 with 3 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“class McsIMU(IMU_Base):n”, ” “””Concrete class based on abstract base class IMU_Base “”” n”, ” n”, ” def get_data(self, in_file, in_data):n”, ” ‘’’Get the sampling rate, as well as the recorded data,n”, ” and assign them to the corresponding attributes of “self”.n”, ” n”, ” Parametersn”, ” ———-n”, ” in_file : stringn”, ” Filename of the data-filen”, ” in_data : n”, ” Sampling rate (has to be provided!!)n”, ” n”, ” Assignsn”, ” ——-n”, ” - rate : raten”, ” - acc : accelerationn”, ” - omega : angular_velocityn”, ” ‘’’n”, ” n”, ” # The sampling rate has to be provided externallyn”, ” rate = in_data[‘rate’]n”, ” n”, ” # Get the data, and label themn”, ” data.columns = [‘acc_x’, ‘acc_y’, ‘acc_z’, ‘gyr_x’, ‘gyr_y’, ‘gyr_z’, ‘mag_x’, ‘mag_y’, ‘mag_z’, ‘taccgyr’, ‘tmag’]n”, ” n”, ” # Set the conversion factors by hand, and apply themn”, ” #conversions = {}n”, ” #conversions[‘time’] = 1/1000000n”, ” #conversions[‘acc’] = 9.81n”, ” #conversions[‘gyr’] = np.pi/180 n”, ” #data[:,:3] = conversions[‘acc’]n”, ” #data[:,3:6] *= conversions[‘gyr’]n”, ” #data[:,6] *= conversions[‘time’]n”, ” n”, ” returnValues = [rate]n”, ” n”, ” # Extract the columns that you want, by namen”, ” #paramList=[‘acc’, ‘gyr’, ‘mag’]n”, ” #for param in paramList:n”, ” # Expression = param + ‘’n”, ” # returnValues.append(data_interp.filter(regex=Expression).values)n”, ” returnValues.append(in_data[‘acc’])n”, ” returnValues.append(in_data[‘gyro’])n”, ” self._set_info(*returnValues)n”, “n”, “# Set the conversion factors by hand, and apply themn”, “conversions = {}n”, “conversions[‘time’] = 1/1000000n”, “conversions[‘acc’] = constants.gn”, “conversions[‘gyr’] = np.pi/180n”, “n”, “acc = np.column_stack((acc_x, acc_y, acc_z)) * conversions[‘acc’]n”, “gyro = np.column_stack((gyro_x, gyro_y, gyro_z)) * conversions[‘gyr’]n”, “time_second = time * conversions[‘time’]n”, “n”, “acc_sub = acc[::5,:].copy()n”, “gyro_sub = gyro[::5,:].copy()n”, “n”, “initial_orientation = np.array([[1,0,0],n”, ” [0,1,0],n”, ” [0,0,1]])n”, “in_data = {“rate” : 2000, “acc” : acc, “omega” : gyro, “mag”: None}n”, “in_data_subsampled = {“rate” : 400, “acc” : acc_sub, “omega” : gyro_sub, “mag”: None}n”, “#mcs_imu = McsIMU(in_file = None, R_init = initial_orientation, in_data = in_data)n”, “mcs_imu = McsIMU(in_file = None, R_init = initial_orientation, in_data = in_data_subsampled)n”, “# mcs_imu.get_data(None, {‘rate’: 2000, ‘acc’: acc, ‘gyro’: gyro})n”, “n”, “def show_result(imu_data):n”, ” fig, axs = plt.subplots(3,1)n”, ” axs[0].plot(imu_data.omega)n”, ” axs[0].set_ylabel(‘Omega’)n”, ” axs[0].set_title(imu_data.q_type)n”, ” axs[1].plot(imu_data.acc)n”, ” axs[1].set_ylabel(‘Acc’)n”, ” axs[2].plot(imu_data.quat[:,1:])n”, ” axs[2].set_ylabel(‘Quat’)n”, ” plt.show()n”, “n”, “show_result(mcs_imu)”

]

}, {

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

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]

}, {

“cell_type”: “code”, “execution_count”: 25, “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”: [

“mcs_imu.q_type = ‘analytical’n”, “mcs_imu.calc_position()n”, “pos_data = mcs_imu.posn”, “n”, “fig = plt.figure()n”, “ax = fig.gca(projection=’3d’)n”, “ax.plot(pos_data[:,0], pos_data[:,1], pos_data[:,2], label=’estimated position’)n”, “ax.legend()n”, “n”, “plt.show()”

]

}

], “metadata”: {

“kernelspec”: {

“display_name”: “Python 3”, “language”: “python”, “name”: “python3”

}, “language_info”: {

“codemirror_mode”: {

“name”: “ipython”, “version”: 3

}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.7.0”

}

}, “nbformat”: 4, “nbformat_minor”: 2

}

{

“cells”: [

{

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

“# McsPyDataTools Tutorial for data analysis: Spike Detection and Sortingn”, “n”, “This tutorial gives an introduction into data analysis with the McsPyDataTools toolbox using simple algorithms for spike detection and spike sorting. Its purpose isn’t to develop production grade algorithms for spike detection and sorting, but to provide a gentle start into the analysis of neuronal data files with the McsPyDataTools.”

]

}, {

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

“The dataset we are using in this tutorial stems from a recording with the [MCS Multiwell-MEA system](https://www.multiwell-mea.com) using a 24-well MEA plate. Due to file size constraints, only data from a single well (12 channels) is included in this data set.”

]

}, {

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

“First, let’s import the necessary modules:”

]

}, {

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

“from IPython.core.interactiveshell import InteractiveShelln”, “InteractiveShell.ast_node_interactivity = ‘all’n”, “n”, “import osn”, “import numpy as npn”, “from sklearn.mixture import GaussianMixturen”, “from sklearn.decomposition import PCAn”, “from sklearn.preprocessing import StandardScalern”, “n”, “# MCS PyData toolsn”, “import McsPyn”, “import McsPy.McsDatan”, “from McsPy import ureg, Q_n”, “n”, “# VISUALIZATION TOOLSn”, “import matplotlib.pyplot as pltn”, “%matplotlib inlinen”, “n”, “# SUPRESS WARNINGSn”, “import warningsn”, “warnings.filterwarnings(‘ignore’)n”, “n”, “# autoreload modulesn”, “%load_ext autoreloadn”, “%autoreload 2”

]

}, {

“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 to your local environmentn”, “file_path = os.path.join(test_data_folder, ‘MultiUnitData.h5’)”

]

}, {

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

“The data set contains 3 analog streams and 3 event streams. The following table gives a short overview over the purpose of these streams:n”, “n”, “| Stream | Content |\n", "|——————|-------------------------------------|n”, “| Analog Stream 0 | Electrode Data |\n", "| Analog Stream 1 | Analog In Data |\n", "| Analog Stream 2 | Digital In/Out Data |\n", "| Event Stream 0 | Digital In/Out Events, Stimulation Events |\n", "| Event Stream 1 | Experiment Timing Events |\n", "| Event Stream 2 | Treatment Timing Events |n”, “n”, “For the purposes of this tutorial, only Analog Stream 0 is relevant because it holds the recorded neuronal data. The other streams, especially the event streams, become important as soon as you are working with stimulation and need the stimulation timestamps (Event Stream 0) or if you are analyzing Multiwell-MEA data recorded from experiments with multiple phases, e.g. if recorded a dilution series for a compound. In this case, Event Stream 1 and 2 will give you the timestamps of each phase in the experiment. However, because only a single phase was recorded here and no stimulation was used, Analog Stream 0 will suffice.”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “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/AnalogStream/Stream_0” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (12, 13245000), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (12,), type “|V108”>n”, “Stream_1 <HDF5 group “/Data/Recording_0/AnalogStream/Stream_1” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (8, 13245000), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (8,), type “|V108”>n”, “Stream_2 <HDF5 group “/Data/Recording_0/AnalogStream/Stream_2” (3 members)>n”, “ChannelData <HDF5 dataset “ChannelData”: shape (2, 13245000), type “<i4”>n”, “ChannelDataTimeStamps <HDF5 dataset “ChannelDataTimeStamps”: shape (1, 3), type “<i8”>n”, “InfoChannel <HDF5 dataset “InfoChannel”: shape (2,), type “|V108”>n”

]

}

], “source”: [

“file = McsPy.McsData.RawData(file_path)n”, “electrode_stream = file.recordings[0].analog_streams[0];”

]

}, {

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

“## Visualizationn”, “n”, “Let’s define a plot function to plot a single channel.”

]

}, {

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

“def plot_analog_stream_channel(analog_stream, channel_idx, from_in_s=0, to_in_s=None, show=True):n”, ” “””n”, ” Plots data from a single AnalogStream channeln”, ” n”, ” :param analog_stream: A AnalogStream objectn”, ” :param channel_idx: A scalar channel index (0 <= channel_idx < # channels in the AnalogStream)n”, ” :param from_in_s: The start timestamp of the plot (0 <= from_in_s < to_in_s). Default: 0n”, ” :param to_in_s: The end timestamp of the plot (from_in_s < to_in_s <= duration). Default: None (= recording duration)n”, ” :param show: If True (default), the plot is directly created. For further plotting, use show=Falsen”, ” “””n”, ” # extract basic informationn”, ” ids = [c.channel_id for c in analog_stream.channel_infos.values()]n”, ” channel_id = ids[channel_idx]n”, ” channel_info = analog_stream.channel_infos[channel_id]n”, ” sampling_frequency = channel_info.sampling_frequency.magnituden”, ” n”, ” # get start and end indexn”, ” from_idx = max(0, int(from_in_s * sampling_frequency))n”, ” if to_in_s is None:n”, ” to_idx = analog_stream.channel_data.shape[1]n”, ” else:n”, ” to_idx = min(analog_stream.channel_data.shape[1], int(to_in_s * sampling_frequency))n”, ” n”, ” # get the timestamps for each samplen”, ” time = analog_stream.get_channel_sample_timestamps(channel_id, from_idx, to_idx)n”, “n”, ” # 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”, ” # get the signaln”, ” signal = analog_stream.get_channel_in_range(channel_id, from_idx, to_idx)n”, “n”, ” # scale signal to µV:n”, ” scale_factor_for_uV = Q_(1,signal[1]).to(ureg.uV).magnituden”, ” signal_in_uV = signal[0] * scale_factor_for_uVn”, “n”, ” # construct the plotn”, ” _ = plt.figure(figsize=(20,6))n”, ” _ = plt.plot(time_in_sec, signal_in_uV)n”, ” _ = plt.xlabel(‘Time (%s)’ % ureg.s)n”, ” _ = plt.ylabel(‘Voltage (%s)’ % ureg.uV)n”, ” _ = plt.title(‘Channel %s’ % channel_info.info[‘Label’])n”, ” if show:n”, ” plt.show()”

]

}, {

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

“Then, we can have a look at different channels (feel free to play around with the channel index and the time range):”

]

}, {

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

{

“data”: {

“image/png”: 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qFaEIW7LrGE5k5Ebt/UpT9HzDgVRM2+gsGDFlwyEk9E0s3fPBY7yPfDZzO177c11E3+PlP9Zi86G0oFHdaNRCkDnoL846ecOhkN9lRnEU3Q1ZECwqPNpHs/MK8dWcnbjtq0XS349Zvhdjlu+LadAtO68gZOAjVn3X275ajId/WB6R187NLyzx2Uc/L06yHZArKFQwYt5OZOeZnwNu/mIh7hqxJKx2yW6GZIfY3SOW4LVx6y3bVBzs9aDmUGpWHr5bsBuKogTqyW074i4zWx3QiGZ/0Atmfcrc/ELM2GRd9+yEP2AfD/WYfl6yB4//tCLWzQjiJlix7cipuNieqgXbU3Aq2/nAjKIo+H7B7pgM6uw6mo53EzcZbsd1+08ioW9iINOz2CqGwbB4E9OAkhCiPHzBpF8VRRnv//ERIUR9/+/rA+BQYITsP+G7iVu772TM2lBYqODuEUtwn43CweEqjtHzcF3/2QI8/vNKR88ZtSgJgO9iHK5lu4/j2zCKI8scTs12dVF2Qk2fjtUu8+H0bcVu1ZDsvIKwO2/x0/Wzb3cUsrpe/H0NLjdYmcmrEVmjKQOx7o9n5xXggremYOhk+ap9sW6fU/O3H0VmrrwA6PlvTkavT4r/9IvUrDwcScuW/u6tfzbaDsiNX7UfQyZtwaczzaekhqwW5oDT/edYRo6r55m2IUZnPi/e9Y2/1uOdiZvw95oDroL/Gw6kRqTQfGpmHhL6Jgamc0fL4p3Hgla1+3D6VvT5aQUW7SyqZagoCqZvOhK3swPe+nsDptkIgjnhVV/Q6LjT/3z2lmT0/Hgexq8KrY0WCynpObj/u6V4+jfzaZ8yy5NOYNDETXj9r+jXaOzz0wp8O3+3YT/npbFrAQDdPyyajRFP0/EpemK5ypsA8B2AzYqifKT51QQA//H/+z8A/ol22yj6Nh9mAeeS6M5vFuPdMJZvl7lk6Exc88l8T19TT+2cxDwIWUyuywdPZuGCt6bYWvlHLtYbOr79tfqAJ9kEZuI1MKPWeflDt+JezI9NF5JSMvDAd8tMi7fvOmocoIzX70jvyvdno9OQmSgs9I2su83myfI/Lz3b3go8q/e6nzoWi92pOO7DepPW+7JKX/x9ravnX//ZAjwyKjgbxovdfFeKL2Pi2yhkkGrbe8+3S/DOxE2B/+895jtvn9Rkl/yxcj8e+2kFRoc5dfKRUctxu0FmabwJty9o91BRjym1fqndxWHW7PNl2uy0mWlzj8OMyJx8X+bpDhcDtTn5vvNgNBdNUfdp9ZpjdExqp7dyumHpFssMpS4AHgDQXQixxv/nOgDDAFwthNgO4Gr//ymC4qGP6qajPGXDYfT5cbnt58ZbZ/xwanZUi2JGWyS3t50R6bf+3oDvF4TXmXR7gewweEagHoQXYnmhTrKRgaM+ZtL60OmViqLYLjqr7jP5BYVxlaoej5xunQMns5CbH/o9WN3UluRvIdqfLd3f+Q53ekA0AhHpOflI6JuIfySrn1lRp+/8u+4gBk3chI+mR2e65C1fOr+5Nh1Nj9YOEquDTPK+BYUK9tkMXu9ITkckkmw8PfVH8Dri9jg8nJod9DfgrpmztiRjxZ7o198ykpqVh5V7jkf0PSKV/aKe5+ZstVeTd7HLmm12pKTnIM2fhb/3WGZQnctI0+/SVrv4oVRJJmpx6jTo2rrhQCqSDbJrVVuYACEVy1XeFiiKIhRFaa0oykX+P5MURTmmKEoPRVGa+v+O7NmplMkrKMSPi5KQX1AYF6Nj4bThiV9WYsZmzYxIm68VD5976+FTuGToTDTvPwVzXBR4HTJpM7oMmxWBlkXHjuR0zNzsPKXa7mgT4KsDMGjiJmTk5GN5kvVpJCU9B4MnbvJkxZ2U9Bx84/FUv3C4rYc1f/tRXPnBHPy1er/1gw28/td6NHljsuljtMdkfkEhmrwxGUMnb3H9nl6Jg1OFJTvns6zcAnQZNguvjXNfl8vsfSIZ+7N6aS9uMGSfbfjM7Z6v0lmcYqQH/FPiw1lhUR0wOZlZDGrixKBjEI8j+u9N2YKu783GwZNZ2HwoDfO2Gd9g6xcd8erTePFVqAWzo3HImQ1+rNufCqAoy8RS/O0Stj30wzLc9tXiiNSBczvw4fT7j4eBrA6DZ6DzkJnYcywDl78/G5/4V6HVXuvsBn29YmezxMO9lVtq26//bAEuf19eXkAV6RkSxVXMi3JTdP2wcDcGTNiIX5d6s0pJNGiLPZYUiZp5/Y//5KzGEQCMmLcrrLoRZqJxPb3qo7l49EfnRR+v/dT5ifz5Matxx9eLccwiqNL/nw0YuWA3Zm89WqwGWOzoMHiGq+dtPexLz16/3/2IzOhlzmpB5fo7oz8v5hLKZpwcp+qUI7NiyG72+Uh1IGWdeqOR07X7Uj1//9SsPHw0fRvuHrHYk9fTfx472237kVNI6JuI7R7UsosVr/YPWdCwsFDBL0v2GE6nW5503FZ9SDvHUWpWXsh0k5JQJ0T2GRbs8NX6OZ6Ri2s/nY8HPVioIVr36NpV19R/KQrw9r8bMXqZ931eOwFBtZ+25VB0juPjGbmBTMhoW+8PnsUyJqN+I07PPfEW3M3ILUCyf8W/NZLz2MIdKSE/i4j42ixRkZ1XshfGiBQGlIoZRVHCCiSonSJtUeM4CMibGjFvFy7732xPikTHjWIQyo+3C6xb6nLo2ZLpPlrqdK2CQiWiNZQURQmsamb5WO/fPiK8aueafScdLZv8+aztnhd9jyYvgsKyfXTKhsOYLimqetLFKjHRHrFdu+8kzus3yXaHedSiJBx3uCrU7pQMWyuN5ZicM8y2i3ZK29gV+3Bev0k4eDLLURBi4rpDQX9b2XwoLagYcDxxuwuZnX4nbziMN//egI8NptPd8fVi3PTFwrDeS/2+Og2ZiTZvT/M/zvuLQjyd57083GPZh9CeF39YmIR+46Nf0NgVBRizbK/rumPt3pmOKy0yLADfoiuRCko47TftOppuOxMy3u9XjIRbfgGI9We3/+bF9CuiMDGgVMyMXrYPXYbNko68jZi30zJFX3tCisWlPj0nPzA32K4l/rnKXmUpRfukfCIj17RzUKgoaDVwqu0RtHBW5Uvom4i3/t7g+vnFmZMb48AqbxE4SL6euwuXDpvlaDpNJOOPbg+H/IJCPPbTiqD90W07tU97eaz94q4fTNvmedH3aPlnzQF0GTYLiySd+raDpqH7B3NCfv7z4iQk9E20zLZ74peVeMzjZZ+jdXO4dLfvfK9OBTY6brXZCM+OXuXoPbp9MMfeSmMmB8dBWe0Iv3u+LSrYqtbm0BZ7jcS2vPbT+UHFgOOB+jkz8wowdNJm1zfJMmoxWFkw8Q8HK2TG8uYnHsaV9h3PRELfRGw44H2mXzyIZCaZt9+fr50ztySj7/j1eG/KVtevlJLuOyYOpRoPWPT8eJ7nqyu73dL3fLsE70/dilSTAQ+rc6bRdcLr/n6/8euQ0DfR8nGnsvOCBu61wf4W/ae4yrYPx/Wfzcf5b5qXHtA6eioHl783OyjTz4r62BEuB/kmrjtoOsU2ErzYPUYv24uEvolIPmVee6mkY0CpmFE72+oKFqrc/EIMmbQFt9lc8UFEoSfz1K8r8cKY4CUy2w6ahtYDp0X8vWWcfOSEvokYOGFjyM83Hky1XLI+LTsvkBUDAG3fmY6bdSOl2qbkFyo4lZ2P/v9YB3qW7DrmaNRV5mfXK3HFJ3V54ESDkXzn+7r2EuP9caIuH2w3SylSwv1k+09kYfqmI3hujL1lcO1mHnlZZFSWKh4vVu/1tW3L4dDMyxOZedglKYY+doWvltUBh9kutjh8udTMPHwwVX7Tk9A30fEqOFbMjuMjae5qhKn0mWJ2ThlmAWrtYgvaGyH1KXkFhejz4wrsSLbOuo31aG9qVp77xSP8Hz1x3SF8M28Xflqc5FWzTA3610FgTYnc4IHDJtiycs8JRwEzq/dVaxl69Zpee/XPtViw3XkmjXrchRNQyM4rwAkb2Y923sNuM9T+Zbg3p60GTEXnobMweb29DEeZFv2nBFbxs0M9Jzo9lJycX6y2oxBAl2Gz8MPCJEdt0B//u1MyMGLezpDH2Z3C32rgNLQyuNfJzC1wVA/UCxsOpCE3vxC3fGlx/6DZDnuPZ0blXlH1zG+rPZli64Q6PTScTznOvwLtHv+KjjuPppfK4BIDSsWUPlqv3lxYnZgNYvjeNEpn0vrD+HvNwaCf5RXEunts36hFSUH/VxQFvYcvsBzZfuC7ZbhuePDog/6mUXaOttMpudvlTZrVyPCuo+mBws2x+IZSM/NcT2m86B3fRXvo5KJMFVkWnJPVANXH2i6kaWDcyv0hN55W0+nmhjFC02/8ejxukJny/YLd6ObPetlyOC2wEpMdXhRkvvjdGYaZNV51WjYdTAtaxvbmLxZ6Xn/twMksJPRNxL9rD1o/2KGDJ7PQ50frzJngTFPrbWf2Fbnd9EMmbQ5a4Wl3SgYS+iYGptp5tQqO+hb6TBQv9hh1O45fdUDawTffbs5aoP3Othw+hRmbj+DOb4zP5/GQvQL4gnW9Ppnn6rn6j+D2+m92jrGaymz52v6/ZceR/n21C0l4kfng5ju+7atFeOVP4+L6C3ekBJaqdyPeemhjV+zH/d85z6RRt62b7+mZ31bh5bFr8eD3y9D2nenG7+H8pS2px4h2quu6/Scx1mHA75T/Orhmv/mgSkZOPvr8uFyazZSZWxDUr9LTT1MLHEtOz412HuTgJQ+czAoMEjgdeFH3l7tHLMaQSVscz6jwkv4je3HOUQeynLLz1vFyzYq1Hh/ORachM2PdjKhjQKmYMUoHdHOiiWbkGbAOatiRmZuPj6Zt1S1/be/DexXMssqgMJqSdu2n8wMrs0k7r+E3zZBZLRAA6P7hXHT9X/C8e9nucfl7szHG5tQ8J5/nlq8WoufH7m5aZPt+uFlw6ojo+FXOl8zWevmPtYHaTKrCwCie/PibsqGoI+n0uB69bC+mSWrnAMCgiZuw25/14sUqFYEOOxTb7TzhwWpPRoGc3PxCXDd8Ph7/OTiglpblbYHSzf7sw79XO983FEXBqr3G54/3p24NXrlSp2ib2983nvxlJdqZ3BTJ/LBwN5J0GVL680GW5nyuQME6/42LV4G2aE9N1k5BeMg/SurFcs1B35nud7ZqP7nYEKv3nggU1PfCXpcrCoXdx9A8/0RGLlr0nxJYlvywf2lnr/Y3O0199McVgRvU+dtTpHXKYu2+kUstVylSyfZJVbRvDpdoAtB2d/lVe0+g3/h1ptmCmzSBYqvzr2riukMYt2o/lu0OXR32t6V7Q2oUyYIWR0/lBE2NslvnLV/ST73x84V41R9E/GfNAaz0MIs3cd0hzNicjPtHLkVC38SQ/qu6aa//bH5Ixs77BhmqrtnKDDX4ucdvnZnj3fTc4qKwUEHnocGrR7s9DTR+fRIeGSUfHDuRkevJtRUA7vxmMVoPnBr4f3pOPloNnOoqq9FLxbXWVzgYUCpm1KwCw4t9HEeIvbj4fD5rB4bP2oEBE0Knh0X6o4d7gth8KA0HTmYhN78wLiP5WTYCfnuPZ6JvBIpb7joaOr1Hq8uwWSHTBiNFQXCG021fLcI3c0NTn+0yGuFysg9Een8x7KSZ7PPqSNe+41nIL/QFLC2X9jV4PdnTsg2yw5YnhXbygaJA3Yqk4M72dcPnB9WvKSxUDJcxP3oqB/uOZ5reoKivdeBklmnNB5nflu3FrV8uwrSNRUFGJ6eVonoGRc8SwldbyWgEe/KGw9Kf62mzXN/+dxPu/MbdCmfaz5PvYvlo2/WFInhMrPLv22bnRLtvH1i+3OICou9gB6bsaH5md7T9li8Xodcn87A86XhIYFDr4MksTwvzfjN3Z9BNdNjTajWBrGVJx5GZW4Cv5/rqc5hNPbfaSjuS09Gy/xTsP5Hp+LqurgDkRZ2you84NncfK/ecwNv+6YHqgi1m++n6/anYcjgyU3W0ZQLsumfEEoxetg9t3p4Wkk0suxad129SyPnXqdf/Wo+kY75rhPY9diSfCrrWawvzA8Cf/mkxRuzuh8+PWWO7tIUTO/19sPnbgwet1XZtOJCGIZO2mGYsqY/1ah/RXl/VTZiDYE4AACAASURBVD1ulfl2DPesox6Lbo/II2nZYWWZx1J2foFhoMdeDaWibV9QqGDWltDBsdSsPLR9Zzoavz7JdTu1lu0+jrTsfGw9fAqztyQjcd1BnMrOx0fTw7vftDPVlYIxoFTCvT91C5bsOobfl+/F2OX7AieFU9n5gROH/kSRlVsQkRXVjqS5m1OqbZ7amXO6FHkk3D9yqe2VKbQGTQytzQQEd+QycvJ1WVjhMbqxO5WdhzwXN31p2XmY4cHorL7TZeTAySxpPRw7qf1qp8/NCleAr9M9dPIW6e+6DJuFO782v/HWH1/q1EIv74dHLdzt+dQuLVlbZ2imfwzyFwO2Wm7VSSfNbQBXlo2nDbZ8OH0rLho0XTpifPG7M9D1vaLRZ9lNiVo8f8vhU45vBNVC7PtOyOtnWRaO1zRI+8i3/tkYGMF2Sh/AUQNz6Tn5+GTGNgxOlN9A2PnkDxuMUJrJ9Z+P1M5ovI70FSoKEvom4n9T5OcGPauPof3up2w4hI9nyFcvA4q+s9TMPHwxewcKDW4C7vh6Ma6UFHdXXf3RXE8L87otxmrkG//rqcEOrXD2izHL9iIjtwCTNdmj8lXeIsvptCyr+o3h0JcnkAV2b/h8gSfZrV5RN1tadj4OmxTJ13Obcaf1myZTW1GAqz6ah7tNprBqmX3fdjKo9K4xmZLqdAEAq33xm7nyY1w7Zbj38AXIzDXPDM7KLSiatSB5z5z8ArQZFH691VPZ9jKUjQbDnPbTOg2Zif94VAdI3yaz/sbsrclI6JuIPcfMB2fNyL57bSa6asA/G1zfI0aiVAAA9PpkHh4etRyvjfMNeDs9d+uzac2muhqJ175KtDCgVIwdPZUTGF1UC97qT35fzN6Ju0cswWvj1uPVcetQ4M8k+HruTrz9rzyw8cLvq9Hz43lBtUgAYNrGw0jom2i6coRKNnIfbvp7vC1jv2BHiqusq7X7Ui07ry0HTLUunufAUYPaNa0GTsOzv+mKKts4KT4/ejX6/LQi7GXPu384N6zn203tB3yd4fGWo1u+i4JVxpTqwMksLDPImAm8nm6DbjviCyrIbpLcOJ6Ri4H/bsKD30W+mOFd3ywOpPuX0RzP6vayU1xzw4HUkMBJNDP2pvgzdo5nhFfI2Q1ph03zb7tZ4JHot+QVKCE1rj6ZsT30vRUFAydsxFaDUWjtdzvfn3b+z5oD6PXxPFsrLao3hrKi5HkFhYEblUhdD6wyCVQ/L/YtbvDVnJ2mQaUygbQy44BhQaESdC4cMqno9WRPeXXcOuQVFGLAhA14f+pWzN3ubkQ8Q3K8rtt/EruOpts6JqdsOGw6ld2r41q2Dbw6BmKVHRTchiLzth3FsfQcFBYq2HgweOU1oyK/dtkZpLKq8WfXycxcR5kaXuwrIujf3p4fpm86ghWaa/38bSlF2Yf+n21yWGhZ1kI32S2yhR20rnh/Nu4eYS/jVH802F0dV79qmVWJieb9p6DD4BlBP9PuA9m5Dgc6Dd5u/X5nqxeqH1f7uX9YuBsJfRNdl+1o1M96VTi70rLzQoJGiqLgXf/Aj5uApBnZcfTj4j2uSlSMXrYXb4axyvSTv6zEJyYDLVpmGUafzdyOPj8GZ5faHdzWm7LhUFzOOIkFBpSKKSEEbvtqUWB0UTuqbkabpXHIYERHnTeuP3mqo/zzt6VYrlClr2MCxHY2nt1RZDNedmDtnIA2OkwBn77pSOA7y84rCBRgBsw7V1MM0r/N+hHqagb6fUStb+GE0ei6V7QpvHbmVStQAvU5zKRobr7nbz9qWDtLlVdQGLTiylv/yAO6+rZYUT9fNIpHLt19HEn+776MZJey2q+nbDiM6z9bgJ+X7MHXc3e6+u69mnuvl2zwne9OyUDHd2dIR8Dd3rRon5VXUIiEvon4dt6uoKwvs+eFMxJmlpHYfvAMwz1u3Kr9+GfNAQyfuQOjFiUFAqN6+kzUaz6Zh+fHrMFWmyOaZvvQ/SOXokV/X72EQgcbISMn39a+ltA3Ef/3x1rp77LzCoKmTqpLcwO+oJIR9eOYtXfS+kNBWRNWGRTzth3Fgu0pgRVq8jSBAru1WmRT4d74az1u/HyhZaBfURQMnbQZT/yy0nBgCvAuoOTkuwaCbwbtriAVDzcFeQWFePD7Zbhv5FKMXLALvYcvCApiODFbMt3k9q/tT5UKd3v0+XEFFjiYTqkfwLRFs1voz8XrTIpRuxncfOynFbhdl43s5S7jxVXNaNrgnmOZWLLrODJy8h1fP2WPtso+sivd5DsfMT/4nGr3O3OS1WPn9YUQ+NJ/fnc7COhll+WGzxbgivfnBJWe+HvNgUD2s9bsrclYtvt4SFDwW4NMUrNm2pryZvEV9dOVy1i77yQOnMzCS2PXYLuN/sHkDYcDg1wT1x0Mml6tl6SZvXAyMxcj5u0MbIcPp28L6WsdS3c3xe2JX1a5el5JxIBSnJq37SjSsvOQm1+ITQfTcNVHcwPLjQO+C5ms0+mmE6A/T8hOrLn5hcj1jzi8Om4dLh02y7SDvtukboMTBYUK5mwtGrHJLXA3QqDt8H+3YHfIPHGnwkkrBeTbOJybxPX7U/HYTyswcIKvc7/nWGbQd6BAsZ0Svv6Ab0TnyznB0/lmWtzsAsBtXzmvu/Lzkj2mvx85P7xpFNrAqZehCO1Utwe+W4abDGo8qd/rh9O24clfiy4+Tjpl+s5yWnYevp23y/booRlFUaRLnKak55gufWrWwcsvKJSO5qmjr/3/2Yhhk7cEpjU5Ccz8utReUXit3PxCjJy/S3qTpeposCrHz4v3IPlUDiau86VqJ4WxgpKMeiP1+ewdliOg6iZPzcqV1r6xU7tCn/loN9X/ZGYenh+zxnAqVlGtIH2bgjuK+09kIqFvouECE9r3Pf+NyUE3BEs1RXL/WWOvKHp6Tj5aDpiK9yTZpE6uA8+OXo0emkCL2bU2TTPNYrbm+mV0tJrVajK7IVK39XbNzYTdAuzac5HK7rE1Ye3BwHS0/QbTNwHz4/qRUculNxH9xq/He7oBIGmGks1T31O/rkRBoWIYWDJ6ncOp2RELXoe2QUFOfkFg1H/n0XSsP+A7lq2ygLPzCtD9wzlBRa2B0Js3AFjnIFtjjWY1qOy8AlvXf+0xoc0wnK57riwL54Np9jIP7IpErUcjsn3IKmtIq6BQsR0INjJz85GQ1YUBYM7WomteywFT8cZf63HxuzNCHqfSf5ZDqdkhj3/dxrZdbTNT5uipnMCKdCuTTgQGdtT9P1xm5wn9MaNq8/a0oOzNQNaei/e3G9C2Q1GKBnO1jAZ3Hv5hOe78ZnHI4PS7k3zZTAMnbMRgf7kC3+uHbqxIBtpv+mIhugybhfGrDuAhyerZWbnGNZ1+Wmx+36DVd9x6DJm0RVpk34y6oq8X5T1KAwaU4tCx9Bw8+P0yPP3rKgyYsBHXDZ+PHcnpuPfboloHb/xVdEJ3O4JlRL2waQ/jK96fHXID8JXDQsWyGjhWabH6EYFflji7mZSNxv9vyhY8oJkepJ+et08TqMvIyQ98bu3J9or35wT+/Y7mhGyH08wjO9TtZNZhNBux01JvbtTAUkGhguy8AjyqSRG108222+HXZzTkFxQGFdg0quGit/9EluUIUl5BoWXNKLvtlk3JMfO17ngJ5zo9cMJGvDtps2fFH2XHYYfBM/Dt/N0A5J0K2TLr6uP++/NKXPDWFFyhm5KoX51GLeYdaT8sTMLgxM14eNTyQJ2naz6ZbyuFXb2hl42mGhUOV/21en9Q8Fk9h8zaklxUw07zeLv3r4+MWhGoV6MN7F3zyXws1XSS90kGHTZ41FE3sl0yUqql7gN/rNyP5LRs/Lo0uGOo/Ty5BsfqHyv2BW0rdXQ2OS0bJzN9K8gk9E3E1R/NDZwTJkgCUFYrdmrpsxtDl3Q2//Lcxn61gyFH0oqyIhVNqMnN1GvZ8WuXOsUeML9h0p83tO2ctSUZV0umTYxethdfztkZtO9qg2rqdjYKtP2z5kDQfjN761G8+fd6NO8/BXkFhZix6Yj0mfoAeb/x7mqSzdx8xPa1VvueWw6dCh4EsrnD7EhOx66jGXjil5WOp/fIqNtVG5B5+99NQdd/IxMM6qNob4IPp2Zb1pixexOr/Z6d3Pgu230MvT6eh+y8Ahw9lRPo82bnFUgHSo1Wtv3WP9gly6AbMEGeuVdYqIQUELcbHDezaKc8OKIPbI1dsS/o+LVD//g9NmpQyQIEgC8LVHtsa4NVD49aHpg6pw/2a78D9Tt64LulgSnK6jfQQ5ddqf1u9h3PxOXvzQ6U7bh7RGjNKwXB9x1BkxpddNqa95/i/Em6d7ciO1Vo+7myPu8dXy/CqEVJGLlgt+v38JLsfNe8/xQ8P2a15NFwNDp8Ksf3fRr1JwB55rsaVO/z0wr8sNA6EUH/GcJNPChuGFCKQ9n+9PUdyelBNwda2hFQfWfy4R+W4abPF0ifly85aIwKPWozWmTT45x2YmUZVW0dFN0buUCeqfKH/4Iiu8EfMsk4GDFt42Hc+fVi9NV1HLXTB1sOmIoHv1+GfcczpdsO8GU8OaEo7lY0MaNeMNQ5/CFpv0rw+ffj6dswzqJWiHqz/fyY1bjgLflF0e719cXf1+Byg2mZ+q1685cL0exN3/uZZcjILDboVKkmrjuEVpolRu20J1xGryerX6KnBj7187vTsnzH/5G0bOnI5sj5u1ytsOWEbARWzUiY6c8Eko2maanHgdNRsE9nbMeGA0U3T7O3Gmce6akj/vmFCrYbjOxpqZ3pT2ZsD9nWnQyymlQv/r4WvYeHnosX7EgJycyzk05vZzNpO/t2p0Jrucp6U4raZndaQEFhIR7/eSXe+GtDUEF5fedXlln5iqQA+bLdx9FxyExcNGh64FyzPTkdPT6cAwA4mJrt6Wixfp+1uklTAORIitbvSE7HTpMgnBdJMtp+xMaDqSGrXG09fAofTnO3Ks6KPScwdrl8kQwnU9U2HkwNGgnW7ruKUlQLZcZm82Ltz49ZExIgVxfxuHDAVPT5aUXgxuDdSZsD5+fDqdkYOGEjFu1MwQ2fLZAW97dyMjMXj/64Ajd+7ssCzC8oxMAJG3EkLRv5Bb4sSX0wQaUNHGo/m92pPicz83CDpt93OC0bd/lXanzq15W4+iN79Qpl2Q57j9u7MVq4w/z6C9hbTVZRfDf/Vn0Ut6ZuPIKtR3wBvIvfnYF270xHXkEhLnhrSmCBCS1ZtlN+YWHg2My3GBjVeurXVYEpVMNn+bLA7fQFAKDnx0Xfof486dUNfzRqipldl46pA9q6ZmiD/+q1c/72FPzfH2txLD3HcFaE9mV+W7YXe49nYoCk3IDZUVaUoRT5ebEvj5VPuQbk9296BYW+83nTNyYHfiabSr9csxpuUkoGWg2YiqSUyC3sYkX7yXLyCwL9kInrDoU8dtfRdMvapYDv/Ls7JSNoIMZIH4sVO9/+d1NQIoKWul/cNWIJFmkyx3sPX4BfluwJqU1ZUpWLdQMolHog2T11aacBCIhAir3sxuCv1aEjIcuT5KO0138mD0rpKYqCHxclBf1M2wc6nJqNM2tUCvr9yj3HMWjiZumFtLBQQYHim6K1WpPVZNRZUUfqFcU3jUi73LXREuOAL+Xfbkr79uRTphfBL+fsQHJaDgbe2NLW6yWuDz1JAr7pIA1qVgn8f+K6g2h1dg2ce0ZV09cLXXI62M1fLMRDlyYE/v/pzNBiu3pqJofshK5KzcrDst3HUaFcGZx9emXDx6n73a6j6SE3SNrddMH2lEAGxdO/rUKiyXvLpoxtOpSGiuXN4+TalcgKChXsO56JhNrm21cvUksn+4Qe+QdTs7HnWIZmP/BtNHVFCyC4psvgxM2oWtG70/vxDHtBAlldJTPDZ+3ASz2bOb55+3jGtqCpVw8bjIZaOWZQmFsbFNFO6bE7nUgrPScfd369GM90bxLoLGs5uRHYKSkWH04Xd3nScVSrFLyfqM1xWmfE7sPV7TlJs8KW9vyln3al3pxb0Z73tdOEtMd78/5TsHPIdfYaqrHnWEbIjbD+5mKMQVBFVagouOfb0BHxq2zc6C/ckYKyuoPLycDEXSOWYOeQ65CdVyANcN7+9SLbqyGp7601bdMR3Hlxw5DHGa0IJSNrV+D9AOzzYBVL9TyjPZ997x8QUm9SR/n7Mg1qGl/P9JJPZaOsECHTQf9YuR+jFiVhz7EM9GheD4MTNyNT0udREHojn+y/CXp57Bo8N3o1Lmp4uu32qNS+ofZYM2OUKWMnUGTXrxZT3AHfjaC+vzVlwyFcc2F9z9oBBO/HaoBmlK4va0QNbALOgjDSmpU2LwLaYJ82YAAAc7bZG1TR979+XJzkpikxpa9xeb/FgiQ5+QVYuqvoXmDapiOGA27agSrAd81Xz72Rmv41cMJGvNm7OZbtPo5xJgvH2AnQy2oAfjHbfDaJuhLoQz8Yb0e3gcaEvolIfO4y6UJNWto+QLM3p+CFq5oaPtbuYj5DJm3B9wuLBvxP6NpwMjO4PxZyH+Xi+9YGn9Nz8vHm3xuQuO4QRj9+ifMXK2YYUIpD6jnjoINlUGXemWhvqlC4Zm1JxsB/jad9vTZuHX58pGPQz974a4PhHPP7v1tqmL4roz3Hrtl7MmRKh1EhZlkwqc+P8hvTR0aZR6/fm+Ib3f2PJmjjRlZuQVAg8JnfVqNS+TJY0q8H/pUEVxbuSJEu+6xfvj0nvzBQ88KMPkXTqM6POlXpli+LinzWqVYx6DEHTobeAMguBNoL1Utj1wT+bRZMAnx1ePSG2wiUqaZuPIz//rwSADDn/64sao/BRTuvoBDly5bBX6v348XfjUeR9FP4dh/NwPWfyZdavn/kUnxy90WoUM46WfSaT+Zj8zvX4ODJLMM581pGx9DfuqCyPsgxZ2tySFbIuFX7MfjmCwP//8hlNoPM07+uMq0nleIwPd8Jo7R8daqfVUfMrmVJx/HC72uk2WROOmqy7J+QTqaDft8dXy9Gu3OCb1TV6QhmBVP17JxbAN8oXxlJL012kx0pZp1mmcOp2UFTnFVldIfsR9PNa8Co5xo3vp67M7BinkpRQm+srBgFRGXBpHd1U40/mrYVw2ftwP9uayV9jU0H00LOJXbqyDw7ejW6Natj+pjdKRloeVb1wP+fH7Ma/6xxv/y09hzvJhNJr+O7vkzFW9udHfQeah2jjJyCwEqY2uNK2w5tgDK/UAmMwKvZVvqSAfd+uwSLdh7D69ddEHb7Vc+PWWP9IJuMagLZmWJTILkGP/HLKqx48yrUPq2i5Bm+mz+nt7zahTXUYv9uRCoIcyQtGycy7dVWsrsyrZ6bEgyr956U1vCLljV7TwbV9DKbvrvraEYg471r09qBnzfRBeTUw08/gKo9P0QqP2nUoiScV7tqyDTJwYn2yml4lVUmG/BSF9OQ7eP6VSONBqHMBgtUybp+3vhVRf3UExm5OL1KecvX0Fusm+Hz3OjVQdMtLxoUfD3s9YnzlesA60U07B7DxR0DSnHISUfejDYya2XlnhNof25NW5X2tXYkp0uzjLQphicyc0MKOso6moqioKBQcRRMsrLhQBru/y404GJEO+rkhnZlNTcmrjsUMs0rO68w5MSnkgWTAF8Qz40vZgcX4r7e4EKQLEkh1U/5sFvvyskotpbd5b1len08L2jVqaOajqVR51A/ImhEX7fG7DhcsCMFHQbPQNUKZQM/G21QryErr8B0VQu9fw3qWbzwe/BNg375V6Mgi3b6qJqqL2ujU4nrD6FedflNAgCMl2RVFkdGU4vtTLszow80rDtgr36LatXe4Mfb6fy5NWpREh7pcl7Iz6/9dD5+eqRjUIc/UvTby8pH0+XBU3UaVTRsc3hdlvl+wW5HwRN9fTb1mH9t3HpJEENxXEtQ9e/ag4bnKtWO5HQc0QS5wwkmAcBxGx18s2LjWtrrnvYmSBv0X5Z0PBAgWqWZuhPYFxXno+FqP2nIpPBXsI03RlOL1P136+FTqF65XMjvnBa2ftCilpNddm/nxxtknxg932pKdSwZ9T21RszbiUsanYHWDZxl1+nr6ukt3nUsJFhgR5rJdGx7q5iFH1IyquMjq7m1eq+9a/n8bZEL7qnbRbZStj6j0e01QEYbpBkyaTN6NK/r+DVkg8NmpVpkK+XpzdoSOn1QO7Cjz3rytcPyZUsEBpTiSH5BIZJP5UiX87XLzQ0dAMzdmoz259aUFsk08/ms7fhb17nTH8Tr9qfaKuh4Xr9Jjt5b5l4bF7l4Zmcqmh2bXBZc1T/PqPC0F6O6MvpRikjRL2H+iWaqglVnxozb5XTt1lCIJVkdNT07mVMyRuWe9nswzSVW9DUujBYgmObxCiJOFy6IFw9+vyxoWm68GLsiMnVcnJDVgHDaR33XpJ6gU9M2Bu+zR0/lYK0HBaHNnPJooA3wtoNvtGKW0dQVWSH4h0e5m7JbUhn1Y7sMm4WkYb2lmQSzwhwMDIfdzOiXJPVx9hzLMF0xuThTg53v397a0fPe+GtDJJpj2r8cYSPL1osMpRSXy9MDxgEmt/19J2QDMW4Hrp36Y+X+QK1cJ5ystCjzlGQl1Fcl9Ru10hxMHS9pGFCKI+9O2owfFibF5L2Hz9oRWNXLCX0wCfAmMETuGS2FakekV4CKV9oaEUY1xewIJ20+3skKO8q8/a98dRszKQZFCy/7n/Oi0vFi5HxnxfpLC7OMPbv1Swg4aLGcfCTpgyKRDiYVR3ZuUKOhpC15bTQl3cuAaTQ9P2aNrSnv0WK1Cq4bskUUYsHOoJiZS4bGb8ZYLJTQOKgpNwFB/QB2SSVcregSZzp06KCsWGGdARPvOg+dGfYJj4iIiIiIiIhiK2lY71g3wRNCiJWKonSQ/S5+wuLEYBIRERERERERFQsMKBERERERERERecjpggHFEQNKREREREREREQekq3+VtIwoERERERERERE5KFjzFAiIiIiIiIiIiInFu90v/p2ccGAEhEREREREREROcKAEhEREREREREROcKAEhERERERERGRh0SsGxAFDCgREREREREREZEjDCgREREREREREXlIlIIUJQaUiIiIiIiIiIg8JEpBRIkBJSIiIiIiIiIicoQBJSIiIiIiIiIicoQBJSIiIiIiIiIiD5WCGW8MKBERERERERERkTMMKBEREREREREReUig5KcoMaBERERERERERESOMKBEREREREREROQh1lAiIiIiIiIiIiJHSkE8iQElIiIiIiIiIiJyJm4DSkKIa4QQW4UQO4QQfWPdHiIiIiIiIiIiOzjlLUaEEGUBfAHgWgAtANwjhGgR21YREREREREREVnbdTQj1k2IuLgMKAHoCGCHoii7FEXJBTAGwE0xbhMRERERERERkaVjGbmxbkLExWtA6WwA+zT/3+//GRERERERERFRXCsFM97iNqAk2/ZK0AOEeFwIsUIIseLo0aNRahYRERERERERkbkypaCIUrwGlPYDaKj5fwMAB7UPUBRlhKIoHRRF6VCnTp2oNo6IiIiIiIiIyEiZeI22eCheP+JyAE2FEOcJISoAuBvAhBi3iYiIiIiIiIjIUv0alWPdhIgrF+sGyCiKki+EeAbAVABlAXyvKMrGGDeLiIiIiIiIiMhS/RqVYt2EiIvLgBIAKIoyCcCkWLeDiIiIiIiIiMgJwRpKRERERERERETkRIWyDCgREREREREREZETzFAiIiIiIiIiIiInypT8eBIDSkREREREREREXhIo+RElBpSIiIiIiIiIiDzEDCUiIiIiIiIiInKkFJRQYkCJiIiIiIiIiMhLnPJGRERERERERETOlPx4EgNKREREREREREReKgXxJAaUiIiIiIiIiIi8VKYUFFFiQImIiIiIiIiIyENlSkG0pRR8RCIiIiIiIiIi8hIDSkREREREREREHuKUNyIiIiIiIiIiIh0GlIiIiIiIiIiIPCSYoURERERERERERE5UKFvywy0l/xMSEREREREREUXR1S3qxboJEceAUhwZ92TnWDeBiIiIiIiIiMJUtgynvFEUtT+3VqybQERERERERERkyXZASQhRVQhRNpKNISIiIiIiIiKi+GcYUBJClBFC3CuESBRCJAPYAuCQEGKjEOJ9IUTT6DWTqHiY/uLlsW5CidWlyRmxbgIREcXAda3OjHUTiIjIIxecWS3WTSAPmWUozQbQGEA/AGcqitJQUZS6ALoCWAJgmBDi/ii0sVSrV71ixN/j5ovOivh7REvic5fF9P2b1ovfE2TnRu4CMtUqlfO4Jc41rXsaBt/cyvQxScN6u379BjUru35urMx6+YpYN4GoWPn4rjaxboJnlr3RI9ZNiKrzXV5bdw+9Dp0bnYH259b0uEUUaSMeaG/7sS3Pqh7BlpRcjWpXjcr7CAH0LOaFiSc+G9v7C7vf1fqBPSPckvhwTq0qsW4CaZgFlK5SFOUdRVHWKYpSqP5QUZTjiqKMUxTlNgC/R76JpctZNSq5et6NbdwHhco4LBb2dLfGjh5f+7TQoNjift1tPddpJ7DlWTUcPb44ePeWC3FmdXf7hapzozMw+vFLXD33GwedOrvGPXmpo8dPf+kKnOdhx6dmlfJB/1cUz146ahrVOQ3T4jwjTr+dtX546OIotgSY/HzXqL6fTPmyJb8wox1tGtg/T/e4oK5n79vpPOdB9bUD4q9zPuCGFqhbzf01QXtNvvDs+L8Rr1OtIhrVOc3Vc4UQGP34JbauOYNuaunqPUqDW9qejVd6NcP3D3UI+7W2Dr7G8jGPX94IFyfYryvas0XxzGDbNvjaqL7fGVUrBP/A5SXps3vaWj7mzd7NMf/VbgB8fayrmhfvgNJZp0d34PGihqcH/b9e9Uq4slkdy+edVjF4ELhDFILp5VwUnQ633z3v1W4hn9XIb491QpcmZ6B5fWfXu3dvudDR42ufVnR8/fZY+Tlf9QAAIABJREFUJwDRSQyJB4YBJUVR8gBACPGz/nfqz9THkHfK6m44ygh7B+k7Nzvb6fW6Nq1t+7GtG5xu/SANWVpj/Rr2Tsz/u808K6VRHW9HV65peWbcpGFOeKYLpr14Oe7teA6aOWjT6ZKb+E/uvijkZ//X83zL13qs63m4tHHwvqHtFFycUNNVoKe+g8DpPR0bOn59K1b78K3tzg75mXY0xEnW1lNXOgvANqlr/8bJbNS+TUNnx6mVpa8bZ0TUqSa/YK7ub3wz7uRzesHJftr/+haev//Pj3a0fMxNmmzRSG0fq3MqANzb6RzXr2/n/Pne7fYzhe7pGNqWhrUqo+N54S9icXv7BpaPsXkJDkvCGc5GWh/uch4A84CtmRVvXhX499NXNnH1Gm59dV87V89r6CKLVD9wZZUh8WDnBMfvYaRSeW/WvGls0ce5/xL5sXpNS28DLA92PhdPd2uC7heEHxSoWM66HOvT3ZpAdr95rsNjxS0nfcs+l53n+n0qlDPfT/59Rp4V8/X97TX/boffHutk61z1Sq9mjton885NLXFDm7Mw8Abz62Sfro1imv3dyYNrhFMtHAYszPS79gJXzxP+HaHdOadj3JOd8dtj7gaTn+thv7JN03rV8N8rGjl6/etb17f9WG3SxLUXFp3bfnzEul8F+Pr8v/a5xPHAopNAdcuzquN9Td9GvWcoV6Z0rH9m51MGDdn4C3N7n7JAUr1b1cfdFze0PHHXqFwey0xu+GS0F8xnuvk6lRcneB/JDqdD3qRu8M3JAP12kPQ4rC7Q6oiJzD1h3EjZVc1GRH3ToF5o3eB0nF+vGoQQgQCGnRsIWTZTzSoVQn72dLcm2Ph2L9PXauwfFVZTz6tXKofnehTdgDzQOQFDbmkFu4MTM166Ah/d2cbRPjH01tb2H+xS1YpFHdz/63m+NLX4TE0QrFJ5++sTtIhRKr7XU1nrmWTJzX3lSsPfmQWr3WZkqqa80NV2B9nNeajV2cGZNG/f6DyDYfkbV2HZGz3QtWnwyOJ/Op8b8ti3NIGsIbdYB37cuOvi0HOcNth1ZvVKePIKZ0FQoCiQ3cvFjewV51uPumrNf7V70Ej7ozZv6Crqrg3671emQllvOoPv3HwhaumzA/z0wcNo3DT3aukuMOBkMEDm2lb1XU1PbntOTXx5Xzt8eEcb28/XD1yNeLADkob1xhMu9m8jC/t2DwmoN6tXzbPMNllQRTsKXq2SvE/w4Z2hQdst75hnBmn3CW2wdcPbvdD2HGd9Q+15380xVKNyeWm/QtaXAYDWuqzHcU92dvyeqj+e6IxZL1+JpjaD+g0jOPWmlUE25zWam+oalSvg0sa1Q7OPJPQDTY01mX9n28zAecAfeL23U+g1zIjdbQmY913u6XiOtI9f2d8n097X/P5f833ArE9QrWI5vHiV9aCrXqST3Y2Od72kYb0x/qkuaH9uLct7IiMvXW3/8wsAVco7K4/xTPcmmPScdYBnyC2tMFyTEff5ve2weZDvXGYVcNe2zw0n/cbE57qimySjWimOUyBcMCvK3U8IcQpAayFEmv/PKQDJAP6JWgtLGf1+V6ViOQy7rTXqm5zo1VG3urobPn26pN5TmpHJBv4L4tWaETyj6WbaC/cz3Zrg83vbBj0vkspqehgXnFlNevK2ikA3rFXFMDKuv7EZ9bD1tBync/fL6bLQZkpq4VSpEHxifvfmVuh77QVY9dbV6N3KPKovbJ4BhRCoahHcUrevmg2gf+3LmtRG58ZnYNbLV4Y8V3YRa1L3NNzargGE5PTutFaT3SLdu4ZcF/Iz/Sbqd13zwL+Nzv3amwan6b3PdbefBeD0wuc0u2HqC95Pk9Pur/+93DdK9do1vtG1nx/tJA3QVa1YDlUs9j+zzfzbY51wwZnVgzrEZtzc1GjPgU3rnoaGtXznYbsdbyF8+41selJPSeBFNj1YSz1GujatjV/7dLLVhv7Xt8AHd5hnBFWpUBQgXfJ6DzSsVSUk2Kx+5g8NXuvihFqY/HzXkFFN2elI/zMX2fIBX97XTpppJMuUOsNi+8oYBY/NBiakFMX02B6jmZJsN5vuEpd18fzNARD8XdiZTvGMg3OZE2tNshnVG5vrWtXHbbrvWh9IUP31lPEUN8XD276zT68cEtQWQp6Fs25gz5DpXpc2dv4drnjzasvHyK7tVgMhX9zbLjBopz0m7U4r0dJOlX+0q7MMHvX6erpB8EhGew4D7PeD9C5tfEZgqt30l5zVKdQGrP98orPt64QTX93XDhOe6WLwW+efWZvtoe+bWrGziYUQ+OXRThj9+CVoUMve9njRQSBDpQbl/3eb/QHI/2gyEvXf1c1tz5Z+Pqv+lpfBA9k+PPim8GajmOlrkhGlJh14SQhh69yiz5guW0agsv94t3OOePzyRiHnB7tOr1zeNFtcDe7Lpua7PQcVV2ZT3oYqilINwPuKolT3/6mmKMoZiqL0i2IbSyU1E0XdHTudVytoVEpruMFcZieZEWefXhlrB/TEY12LUhZHPiifK1+/RmUkDeuNic9ehhevPh/Xtz7LVRq7VYf8Sf9UoboG02mmvHA5+ksyt+zcYH5+bzvDERPtSeDKZnUt0zgTbUTYgaIiyvrLTcIZ1jcPNaqUxxNXNA45QclukmUXNLfnNfWlZNfIVmfXMBxx9z3J+Fdu26Nd6UcWlJLR1gi7s0MD6ei2/qIm+7yFhe46CjUql0cTk6lpRintWm3PMQ4Ol3cYKGl2ZjXX2Ql2pgjZ7dPWqlrBsvO1493QYKDKLGNKRgiBBzVZQZUdZJkBvt35yvPr4pVezTDJRtp00rDe2D3UOJOiSxPzacayY0SdivfZPW0tg8GqxnVPs/zeZF+D/vV7NK+LpGG9Q27oVe/d1hrN61cPCfrvHtobScN6SwO7qvsvsT/Sbdefupo54Ux/sFPzxQ7td3pXh+CpvNrgkN3T48d3hU5lNvKe7kar6CsverdBNxbdrAy7NTRDrl71iiir+RDVPViw4YIzq+HCs6ujhi77VhsQNMvMbVG/OjZIMm3bOJya71T7c2sGMinu7NAwKPNDzVhZ+noPLH29B8Y/dSnmvnIlqlcqHxJosrwWRnFwu1zZMq5vvPT0g2JumdV/MZpqHY7LdBm1vU2m5WgHtX57rBPmafq0HRJqYWFfe7VC37/dXhDkgzva4NpW9UOm7Bf1X+Q7S80q5fHLo51wVo1KjksU9L++RdilJS5rWhu1T6uISxvXNu3vVKtUDvdfcg4a1qziuu6PPgvVjHbwQ7vfrxvYEwNvbBk4M6qDo1UrlsXzV5lPA/Oq1ueTVzYOuYcrV1aEnCed1CNUyWYx9G5VH09c0diwLqfRdV9ldh7TbmezgQM73GTvvn5dc9fBnXJly2CGLrB8S9uikhhn1/S1RzbQUssf7HrscmdTAYsrswyldkKIdgD+UP+t/RPFNpYq6sGiThtQj4HTq1QIGZV6rkdT/PRIR0fTb8zUqFze0UF34dk1AjcP5TQ3tbIaPjKyUXBV0rDegQwHs3o33ZrVtTW6LVv1YOJzl9lKEe53bfOQn+mn3s146XL8/bTRqJGPmqod7gBGm4ZFF5AXrm5qOR0SCL1BWampoWFGX0TYaPdw+pFMA1EmPr/H21OPmvFRpULZQHDu3NpVpZ/Hzfc2/J62uMwicKBPaX+oS4Lp4z+6s42tqVdm7bUTjJvuYcHvrzWj1e/f3jpk6oVRgc8yZUQgsGzFKiNTz+xUZ7TpypQReLpbE9So7K52jROy5t3RoSGShvU2HJUbcksrVzUy1M+r74zbDRg8dWVj1JQc09pOujawq/9sdrPMjMj2dX2Q2OrSZrbwQcVyZS0DkJ9K6tQBRXWxrmpRL3Ct/r+e5+N/t7cOCfKcUbWC7cxLwNnUW1ldOCB4u5zj7380ql0Vd0tqV819pRtuuqjodZa9Ib+OPGxxDtOa8sLlmPis84L5ajaxorjLoHFi4A0tQvafEQ+0xyd3F5231BpP5cuKwHSzetUroV71Smh3Tk2cazBwdKFkIRG3U1TceMugXpyXszScvpZ2W4/VTVvS3kA7mUZll76tagZPr5b1sE7Xj9Sety5tXNvxIIfqjg726kRqb2S1jKbFqYQQuKxpbSzq1yPknGH13TSvXx3jnnC2iIqZVg1qGC7Kc369ahh8cyuLhYK82zGN+qHVK5VH2TICN7c9G6dVLIepL1yOpGG9UbFcWcu+0xkGA/8/+Gc71K9RCUnDekuD4FqvXXMBTqtYLiiY8YIkmKXODlE/S3cbi1jcYTCwGg4hjK+x2n6APiCmMgoOa8+FG9/uFZEMe5n3bm8dlJmkrZNmd4p95QplkTSsd6DmYUlndtX60OTPB5FvWun0xb3t8M0D7dHLfxEzK6x7Q+v6uFw3Rctsak20Ks3rzynPdjeO6MtGE0IzkkIvILe1a2Dy21DVKpUPmU5SsVxZLOzb3VbK+b/PXBY01/ehSxPwlmYqSZO61QI3tPoOkEpN0dSPeDmNm1fXzKG+qMHpONfFiIidqR8XJ9QMdGDU4vBVNaOO4UwbkGXVyLaD/oLvdEVClVHR+qG3tsIX97ZDy7Nq4PLzfYEfoxtH7ee1mx1yY5uzHI+M3GdRl+DWdg3wn0sTHL2mXlf/Z538fFfD4uxNJVlVbm8yzq9XLZBdVqVCuUCnVn255vWrYdVbV+Odm1riWd05zOg99T+3c1OpfY6dz6L96rxIZVeDlnZ2CVmAxswTVzTGvZ3OcRWcUaeY6IN3c17phods7GtGRXaN9n39j2ubZBrY2ep2zkXqjYDsZv3Bzudiyes9XHeyk4b1Dgq0aH16d1skDeuN+jUqBzqiD6lFtXXf8cq3rsavfS7BM92b2A6SqDe8Yx6/BA93STAcoS+nO+cOuKEFereuHzLNe8PbvaQZeEL4AliVbWSwDLjB69XSQvcj9XO7ug6ZPEU2veGGNmeFtKCi7jqhHgPv3tIq6BptZsIzXYICwGv6+wYNK5cvG5jablW7qHGd04IyuP58orN0Gr2RcGtiqcwC+na+o1evkQfCteeKtQN64o3eLaS/C/fsfH3r+qhUvgxuaC2v31O2jAj5XtVjzWmWsF36LJ2y4cwNNqDdbkaXuOoOB1AuaVTLdCDIzqI8ZtfI0ZIi015OY1U1rFUFG97uFZR1VL2yu+C12QIqZrTno/bnWmfZur3Pc7LwjxV9IXa3e+2mQb2CMpqqViznWQIFYF5m484ODYOCeW9qAu8XamsvlpL6SHaYTXnrZvLHXh4nOXZ6lQro1fJMdGtWF8vfuArdmhlHm2Ud45d6NsM/T3eRFjqLVaV5u6vxqHVW7uhgnlqZNKx3UMFJu8fz9BcvD+loVSpf1lZqdqsGNYLST4UQePSy86RTSfSf98M72uDvp7ugUvmyWP3W1RjkorCvTI8L6uLSJrVxaeMzgqaCeeXFq84P3ITUqFIeb/ZujtGPXSIdoZHdbDu9wNsN0rhRwZ9ppW9m1QrlpCntsn1K22m0U1tLK1Yzqd8zSKW/t+M5WP7GVWhevzqe6d7UszRtLf33//p1zdGrZT2DETSBWlUr4IHOCQ5WGPO9fsfzfEUnX7y6KHBtJ2DjRYD9qubOlrS/0R90+L+e1llEjeuc5qgoqFoEV//Zq9oIAJQtI5A0rDde0L1fraoVAjUjjDZpveoVHa/uompUpyqShvUOO8NEf7zqp5MBRdvl98fdrXjjhYe7nBf0eY2ClO3PrSUdwa5fo1JIkfvh97TFmv5X45JGZzgK5DSoWQVf3NsupIN+mkGnPRr9ZjUrGQgORspWilJ/Ha3+vP5tnGbAybRucHpIoE+v2wV1AlOOZJmRVSqUDVpNs0NCrbAz/gDnnyfcrE07K7/VqFw+YtlbjWpXxZZ3rg1k6anU/o5sP3vhqvPxbPcmQX3WX/t0ki64IGO14uYPDvsZRr5/yPh19OcgfU0yIXzXh5AFcWBcS3LUwx2DjmU3zDKBjOq7Wj3PC92a1cXHd9lfpVSlDlSalS8Ih5MBLzX429FfK2z8U5fiaYsaSVZbVbvdb9YNrpzjsmh9lQrlbA1guKWfVRJO1lZpq5ckY3lmFkKUF0I8J4T40//nGSFE5PP9yXR+eM8W9QxTqNs0PN2wftLL/mJ3QVO9PO6QyQ6sKS+Yp7SXLSNw18W+mwD9aE+4HcbO/toUNatWsNXR8vq0cFv7BoETeM2qFSw7kHapGVcVy5XFl/dFfuHFPl0bhXS2vCQbdTKbZiI7f39iUFPESSfD6ML8xBWN0bt1fcx/tRsa1JRvh/o1KuHpbt6tIKQya/2r11yAKhXK4s3ezTHuyc5B2XtG2QpCiKDzi/Yz92xRD3daBHXN2ypvbYOaVfDNAx2COwg2jm2jwKTa5FpVK2Db4GtNR+/UOfza1+p/Qwt8dGcbnF8v9Jyg3R5GU8iShvXGyP846/CrHXC7Beit6jVoqVtdu/ve0vZsdEiwDug3cnkDuuz1HlgimUahb5OT33jhfyY1SawyPoym0URiBLyIve3R77rm+PnR4GLs5cuWCZoCKWulUSF1t86sXgnv3lKU9emkbomZxwyKN8uuO5G6cXzVg2XVvVbHf63/+v72nk9ViUpAzuQ91AxIbdBQ2yazGzXtPmD2OcKp23JmDd+2l50XqlYsh5d7Ngvqs3ZpUhtv2yycLFtxU1WhbBnbK3qpZEXA77/kHEfTwSfoahypWeKyaTva78bu1HTVNw+0D1kEoFJ56/OI23IJKlkNp3NqVbF9dhdC4Ja2xv2jZ7s3RTfJ4ga1qlbAv89chg/vMK57Z1Q248v72uEpg+2rfgeymnhGejT3TZP7uU9HrOl/NdqdUzPs7DftinBqEBLw9euvtVhICAg+fkc9fHFQUf9I8SLwTkXs9AK+AtAewJf+P+39P6MYMisUaMc9Hc9x1R1z2zG94EzrAuH/uTQBT1zRGP+93PsbcnOK9H9mNTW8FI3AtpPoeauza6D7BXXRzkFRRDsXY30Ww8o3rwqkuT962XlI0GXJ3NfpHNsrWaluNqgxYI+mc6qd3uYPgFQqXxZf3NvO1RLB2s2vjl5Wq1QOic9ZF+S2cnWLetg06Br06doI7c+t5Xi1PCC4FsuIBzvgvduDj/NI1/TQbh/9jaI2QKZlts89riuCWF7SWapUvqzhioPatvz38kbo1bIePrlLXucpnt1ksvyy6uWrz3edIVS3eqXwRuY0X2L9GpWk9VzsjLwaPaShZlUhu6ny/z57Gdb0v9r1qKrXnBaPl7EqqOrUktd7WE7N1VJvdGUr72lpv0arveq61vXRtWltvOBiRSjt++jr0vRseSa2Db7W8Wuq50inK4Cq7ByrWnYyv1/p1Qzv3OT19ENvDbihBX5//JJA3VAjfWzWLZExqttiR/tza+GPJzqHrF7ptXFPdsbFCZo+l4vd6DtNJpLd793s7Druyc62p2o5bW6vlmcGTbd9vkdTfHxnUbClSsXg8977t7dGnWoVw/4e9PWm1vbv6WldnnrVK+GHhzsavrdRxs3dFzdEi/ry+6TrWtXHq5qMrx8fCX19q8vkPf6aeNrsqorlyjpaSdHI0FtboZsu83zwzRfi9CrlcUkju4thFH2AK5vVtTwfyFglLkSCurq6nWBoSWenF3mxoijau4tZQoi1kWoQxTc7HVOjC8va/j3RZtA0w+dVKl9Wumxln66NMHNLst0meuar++Oz9rw6wt7T5UpdZq5qXg+f3H2R4xtMO6Oc+o72GadVNF3K/YkrGrsK3pixOwql/Tz1alTCrqMZtp5neRPU6kx0b14PPy7eg86NzkBLSUHWcLU953SMWuS7eTuanmPrOd8+2AFd35tt+Pvn/Z04o4KTMnb2CTvfx/n1qiFpWG8k9E20/d79rm2OJ69ojCkbDmN3Sgb6dHU3JQvw1Z755gH5ipfxRBbYMVvQIBqMYk2yny/u1wMA8M7ETUE/P81GgFQbANYWEp/w9GU4cDILUzYctl13rFL5sqhUviymvNAVOXmFtp4TKW9c1xxXNquDqz+e5/i5CWdUQdKxzAi0Su6ihqdjzb6TGHJL6DQeL5fSVp1WsVxItpYbsuyqCuXKYNkbPdDjw7k4lZ1v63Veu+YCVKtUDje0cRYYAoqmWpzMzAXg216N/dPc6mjqHWr3czsLiqjTWN76Z6PjNjmlr1GpZfbtCyHQqZF5HUujrCztecRq2vaEZ7rgzm8WI9vFMX2xJMvTi8Egrfbn1sId7RtiedIJ169h9h0YMvly7NTsUbkZV9A+5UVdYLis7gXv6NDQdvFyJ9RgY7iZT9Gkr3sHAFc2q4N/1hzEPR3l22jora0wVLJypxdk9wv3dDwnEMQy4+WAup3EBa99cEcbzNpyJCbvHW/shNQKhBCBlBEhRCMABZFrEtkRz/M1tU2b+GzRRbdGlfJYP7Cn4/Tjzo3PwAMRWFZaZdTX9SIrwyjLqZFB5+fs0ytbjsA0O9N3g62mreo1NxjlMKJdsvb29g0sg0nn+Tu6ZsXWgf9v787j5KrK/I9/n97TSaeT7qSzdvbOHhJC9oTsgUBYRnaEYRHZBGRfAirKCAKO+pPRwUGUGRzAQUSHGTdWHdkFZEcRIkhYZJctBIHz+6NuJdWdqq57q+6te6vq8369+pWu6lqeVN3l3Oec85zun+unl4zVZ5b3PkdbSiWR/FqQpxHaTcDdJXtPefYN5TsHz+7WC7Nj17Yn+8xRMEsmDNLRy8bpgohO7nvOGqHbz1yhRXlWl8uUL3GXHu5/ypqJ20wBy9zHpWANhPSFZuZT/D4/3zXqgOYGHTBvlNbvOmVLr2C256Tf77wS9ORHMW1qWGuTjl0+XvO9EQvplUI/t25K7I3kXKO/Bnj1VnouKpFNpze9NH3hf1CWGlvp77WzrY/u9BJTUmqK8fQRrTpt50lZp5D3luhobqjrVjR7uI9Csv9z/BJ9KsQVXY5cOi5rcXw/flmC1XC6T01K/ZutuGvQrb7QUT6FyHW86WhpClRsubVPvdbvMqWoAs2Z+8tnV3Xp6iPna/649q0xFnj4OG/PaQFWy0x1WK2Z6n+EwEV7zyjJ8TNtxIA+OnFVl/bwknd3nLVSQ3MUF09P99pu5AAdHGBUXS7paVpRdAZluqKXukdhKnShk54KmYLa63TGEl/iTPPKhGTr1C7EQ+fupIfOzX2tU+x/7+71q7qtxDt8QB89c+G60DqRstWt62mv7Ufo/E9M37KAQDFKXdu650qkhf4fWvvU9zoFspr4OfOdLuk2M/u1mf1G0q2STo02LISh5wEr33DzKPRcsa2lqb6o4cfZpM+HxRbZ7Vm/IYwDXK6TYnqpz9Rjtj7ojrNWdpuLXIiv7DUjZy2KbDJ7ffycxPs11umZC9f1GJK67YeVOcXtc7tN9VV0+6xdJvs6kUnSsQGST8VIfz8f59ge1k4ftmW1PzPLmuga5I3sGdPeV7U1pvW7TOnWm3hrgFV5/MhV4ymoJV5Sqk/GtL9P9uh16spSgyiozH3Ab0Or2ORMughsurD+bjlW94lCmG3ljpZGnbl28pYLgynD+uu3Z6zwvbRtlCbk2Dba+zXqjrNW6nPrpgR6vQ0X7KovZ1mxMb0lDGxuKGj6np/v4+osNd56mjGyVQMKOr/Fu1LM9BHF9a52P2+E83+J8oKye9LP3xudu9tU1dWY9pg5XJceFP3o5braGi0a7xXaTxeGLvC1Dlk4xneR5ElDW7Thgl27tVHy2X/uqF7P72GPULvjrJU6ec1E7TunUxsu2DVr7aCofOfgHXTP2avyP7AQ3qa49+yRWhygQ6gYe8wcrtU5OieDKGRkXm+LDsW1lElYK/a19qkPXKg+yDFvaGuTuoa0aP+5nZrVOUCHLhwTLMA8/EwRb6yv0UHzR4eSlCzlGXDDBbvqCz2m158TsC2CbeVteTnnbjGzLkmTlNrD/+Cc8zePAqFbN2OYfvbIi4Gfd+3RCzV1eH9dcfufI4iqp9KeCEYObNZfXn9Plx6cvYhbXW3v8aQPZPPHphIByR375U9Tfa3mjmnTd3+77Xd9zZEL1NzLqgk1IbbiRwzwn9TI1t7M1wYt5CTW22u29U2d/Jvqa7s90M+75It1/rh2/eCIeTlHVfUsinzP2as0/4JbtsaQoBGJYZ74e3utfMP3i7lGmTqsv2Z7q60csWSsDl88NmtRyqg/9pmdA/TQc292u2+/OSM1J8BUg2yCTBXN93/cY9YIXffAxm7TBr+w21TN8rFaTc9GWya/F4GZ33OufT5dN6yrI7pOk1wjIHpaO32ovn7Tk74eG3S/7lZjJUQ3HLck7369KuvqjFv19j9J2srKQ1ubdMEnZujsnzyiuhrTvxy4vX752Eu9PuewxWN1WIijz4KI8jiUrVZYWKNWsjltp+I6zHoKEmvPzfCSA7fXf/3uL7rjqdd8v0Z6SmwxZnUOyFpSIY6zfENdjT6/2xTd/MRfi3qdSUNb1FBXow8+9D+lMMxFXvIdY249dZmv9m0U03P9KLS8w6B+jTmLeRci1ycUpGh3HB/hkgAJ2MxjxtD+TXrprfdLPiKuEuVNKHn1kn4o6Vrn3NPRh4TeFLrR+yngGBaz1PSEzQFOLGHIVlPg00vG+q6f4vez7W31vW1eM8/fD8lYXjboVLUg0nEsHN/7NLFsq1OUWr7vYdXkDr31/t+DvaaPx3xh92maPqJVO3YN0n3PvL7N33ueJKcM668nXnxL0tak5aBeagxlmwqXy5ASFYSPwm7bDdOlv35aOwXo4c78ftKrIPb2WUrFNVoOnD9qy8W8mSlPzjk0e80eqf+8+y9bpnpdc+R8/W3T37XwK7dueUzPguhxa+vboP89oXuxy0/5HP1UyIXXiau69M1b/qTmhlq994G/2fXjB/fTVZ+e3+ty0pnGDe7ruy5aUEFqfgW9eBnmY9pdIRnffBf2fKIeAAAgAElEQVTld5y10lfNnjBF3cDfa/YI/fGlt3TKmklqba4vaIRFkkwb3l+PvfBWoOcMa23apkhxFHafOXxLB9fxeabKS6Wb7tQ5sI/mjWkPlFAKw+ShLb3uy35H33a0NOrNTcHaQsWKsp2aKd828OvTluvDjz/W6q93ry+X63n5VjKNatXIfBrravSdf9xBS7sGJ7Ize8SAPnr+zU1ZP9f+PVYhjCv+O89aWZLp/f9z/BK9uemDyN+nXPkZG76HpP0lXWtmH0v6L6WSS3+JNDJklbCOvpx+9tkddf0DGwMlXwrV28n3c730kG95foBG/Tf2n1n06IFMw71e+h8ds1ATErCEZV2Bw31L2SPxvSy1BX7/+TWBXiNbvP0a63SIN2x4UkaBvVwNlOuOWbglsTWstY8u2nvblS6Kcd0xC/X6ux/oqB/cH9prhqFnscyepg1v9b20dbbvoa1vgy7eZzstzZOAi3IZ96i259mjBnb7bJob6tTcUNgKa3H1pEbtpNVd+syK8Vpy0W2+E0qSAk0Rue6YRdrwyjt69Pm/FRJiBMJviod5UR5kSlHW0aYJbLk01df6Xt49bunvsrdd/objl+jjgMeEMe29F7IOS9yLA5SL9Lmg54V6LneetTLKcLbxp/N32WaUzwMB215+5Tt89VwVuKf+Bax2W6jz9pymLxRY+N4s39S/5OpZSL0Qjd7qaBM6+uneP2/bkevH8BJNeS1F8r2c5b16dM4965y72Dm3g6RPStpOUinmTaEXUWaCC6sBsVWtmSZ09NMZayeXdKpOsT0MfkL9xPYjA04n8RfT3DFt3QrAFqsxhKWmg4jiciHIRUjBn12Or2fddsN0xeFz9fh5O+uf9pyuKcP6a8yg7t9738a6br2M+88dpY6W8EYWzRnTFntR5fS0sMwRU63N9bp4762F3KOoNbbfnM6cU4xGFzFMPmioSewx3IbPY8ylB83WjSdHX6i5WGa2pb6VFE0yoq1vg+ZkWbkpqFPXTNR/hzjdoJz1dq5LHyMKPVYEeV4lTF3o+d/dWkNp618+u6pL4wb33ZJIra2x0Oq/FCvfimv5FLqdBJ2p52vEXwntMn2oPrduis5YOyn/g5XqAMzVCRhFm6y+tmabqU9tfRuKbqd0toX7PXxj/5nbjKqN0iEh1y8qB4P6NQYagZyrxm1HS5Ou+vR8XXLA9mGFJin4sQDF85XCNbMxkvZTaqTSR5LOiC4kRKkU/YQ/OGJeCd4lPLM6B+q2P76iof1TJ7Vzd5+qz/30UU3oiH/EUBALx7Vr3zmpAsNLuwbpc+um6Ms/eyLmqHqX7SIkyqHHQS5O071G88e16xcnlq5xkqnY/bXYhO6Jqydq95nDt1lpar+5nTrjxw8X9dpSYcmCdC9uMYmscmpr7DV7hK5/4PmiX2eXHKuYJKk+V6ZkRtXdCXlW5Cx3KyYN7nW1r/RU33136NTjLwabbpVNevTjqTtN0s8LqBVZ9vJs9JnHvCnD+uvWU5dHGk4+1xy5QA9vfHOb+wutB5MWdTv1tJ0mauXkIb7rooUtVyKrpsZ8l2jwK66pXH7dcdbKLTXwMhVzWir3VbeC1CuKT7C99MaTl+ndzR9m/dviCYP0To6/FWrikBb94aW3Q31N9M5PDaV7JNVL+pGkfZ1zG4p9UzP7qqTdJX0g6WlJhzvn3vT+tl7SEUolrj7rnPtVse+HbWUerMM+eRe6zHFcjl85QWunD92y3PH8ce266ZRwV90qhWuO2roKkVmqYVKqhFKhF/cHzuvUH196SydGfGGW1ItmvwqNvthpUbU1Fun+3NqnXs9pU6CplqX4JpM0Rcfv9IckufecVaFdyFTozL6ycMXhvXcO1dfW6PHzdlZTXa3+95EXdf+zb+StVdKbmhrbMiW0KhNKOST19LVwfHveuoylZGa9HjAWjmvX927/s+aPa9fU4aWpBdTTDqMH6rgVwVaovfrT8/XQxqRMzw1XKVfoKwd3rV+pprrSzjAohUJWvUN58TNC6VDn3B9Cft+bJK13zn1oZhdJWi/pTDObKukASdMkDZd0s5lNdM75L6SALXafOVxX3ZO71FWQRkp73wa99m4yi5EVe8FRW2Nbkklhq0nGCPRYGqR+LsqbG+pyFiHmQrK7jpZGbVdhc7gvP2SufvXYS4Ealb1tyzedvFQP99LwDrpN+dlvZoxo1SNF1uJZOblDj72Q/TXSScF9dyifXtcwpn5uqRtT9CshSukRg3vMHK49chS25jssT5cfMkfPvfFewc9vrKvRuhwjI3sTVXNl9dQheviLO8WapF87bWjgWpWLJgzSogA14ipBkFWCk+7k1f5rDSVtGmZaz7YT7XP05Ceh9KKZfV1SuvjCbySd55wruAXtnLsx4+bdkvbxft9T0g+dc5sl/dnMnpI0T9Jdhb5XNcu1RHk+88a06cbH/6qGuq0nvTvOWpn4A0gSe/GSPtw4LGGO6Ijie0xvy80N5dvzc+85qwM/J+kjs4a2NunQRWMCPWfnaUP12AtvqSNLwf+uIS2+RlQl7WP5fpZC8z1Ni6lHPW7phFrSvrOkOmHlBH3tpidVl5TejAIV+30nvLkSyFf2mqFv3vynko8GWh1gpc5s/vjlXUKKJDzlOOKzGp25y6TUaK6rH+j1ccetGK8/v/qunn2t8MRnlPItUJL066pSqdRFRqqJnxbH9yW9rVQNpf0kvSXpihBj+JSkX3i/j5D0XMbfNnr3bcPMjjKz+8zsvldeeSXEcBKuBPvcNw/YXr86aan6Nm7NNzbV16pPnovxe85epbvWR7PqxK5eL1ehSTJEK2nngp7JhnUzhunk1RN11i5TYoqoMEn7XDNlJpxL6fgVE/TgF9aoo3889S9KLcGbQGBnrJ2kBeP8FcMuRTI+6Gf72Jd2DvX9W7yL26Gt4a2GesKqLj1z4bqsdTiGxVQzZtnE3ldsDFMl5h1HDmzWV/edmZii26gs6ZXbMtv8cWusq9W67fKPbjt958n614N2SHRbKZukd5DkS/Ak/ePOd72K8Pk5eox3zu2dcftLZvZgvieZ2c2SslVzPMc599/eY86R9KGkq9JPy/L4rNutc+4ySZdJ0pw5c5K+bYcuyoNRn4bagqaADYnwAm/h+Pacmf4kn0iSftKIWl2N6cOPC/uC/D6rq0fx9FtPXaa2vg2add5NW+6rrTGduLp8C+hW+3aUqabGNKC50FVlet+qvrbvTL3x3gf6qMBtNkpJH23mx2eWT9Bnlk8I9JxSfBN+P9uwL7gWjm/XJQdur52KHAni1y9O3FGvl3jq+gOfX6O/vvW+rrt/Y6Dnlf/WXoTkHX4q1vJJg/WNm5/UisnluXR7GDrbmnX2rpO123bZp6xWgwo4vYai3D+H8YP76ulX3tU5u07RPt/JP7kpSfUyy52f1tEmM1vinLtdksxssaRN+Z7knOt1foaZHSppN0mr3NZU6EZJnRkPGynpBR8xAuhF1BejuZJ6t522PPBQ5CCRPvzFndTQo9e2mKKwqA65Rr/s7dUp+rffPJ16nI/9JuoG2M7ThurKu57VfJ8jeypFuTds/cpVd6in2aMGFP1eA5obikjGFqatb4Nefvv9kr5nuSr1Nh/VxVQ5TV+Z2Tkg77SksN7noefeVH1tMg9sRy0NVigc1a23fTzOJE16ZG5LwKmtYY6IPmHlBE0ZVn0lCvwklI6RdKWZpavBviHp0GLe1MzWSjpT0jLnXObV5g2SrvZqNg2X1CXp3mLeC4hTMpsOpdPZ1lz0EsK9oR5Csswbk+ykRxTXOVFfOy2eMCjSC55qSdyUswe/sEZN9eU1hL9vQ63e/SC1nsqw/qlCs5+cPyrOkIDYHLd8vB587k0dyD4Qu6OXjdN2I7In6MsoF5o4xbQl+iVoumWxTt1pUtwhxKLXb9DMaiRNcs7NNLP+kuSceyuE9/2WpEZJN3k9wHc7545xzj1mZtdKelypqXDHscIbKlGlnbOi6JEop17OpOob4TzyI3ccq2/f9rTqvB6hBz6/pmyKnudr+LDlJQeHAZV8VFEYfnPGCr35Xmp6XWtzffCkKJnOqlWJu/yMka3aaVq2KiDR4LiZ2/ostTQ52gQT9uZVCVP6q12vCSXn3Mdmdryka0NKJKVfN2cRBefc+ZLOD+u9Kk16uGwtO19ZyHeQ5FvcFieWrYpNqu0weqCk4mpZ5XL6zpN1+s6Tt9xu61t+F735sCXGh8++eDefskwtTfH0/A7q16hB/QovNt5YYNH/I5aM1Xd/++dQth86NYKprzX9/aP4PrPM7zy9MMeOXYPiCcbzy5N21B1PvRbbcvAlbU6xuyBmyyYO1pN/fUcD+zJ7odT8nLFvMrPTzKzTzNrSP5FHhqzO3X2ajl46TmsCFPHcx6sLUumSlIcY3Z6a5lVJwziDoi0enkLnd6dXujh62bgwwylbs0elEmwTOqizVT44kBRqQke/SBfLiNI3D5hV0PPOWTdVz1y4TjVZVrkrFJ0c/lx+6Ny4Q9hi5MBm3XnWSp20emKscUwe2l9HLBkbawylxu5S+ZLavj9z7WTdtX6lOlrK87xXzvxc7X7K+/e4jPucJK5QYjCwb4PW7+p/6fMNF+yaf3pHUo8MZeyLe0zT4Vf8Tv37VEdCqaujRQvHteuuDa+F9ppRbpXt3miaprrymKJVqMa62i3HgG/f9nTc4cRu3zkjtXB8e6R1vcpNmMUow8RFfHUb1tpHLU11evv9D+MOBWVq+IB4RgUl3f+esES7/cvtcYcRqlztxQv3mqHR7X1LGgviU1dbE9towGqX92rXOVddqfUK01svXaW015OYEKuvKWy4frlqqKvRNUct0Jizflb0a5ViszzvH6Zr9uiBWpCglbMWjW9XnwgK74bZU1/uzMxXMimBh5SqM3loi55/c5MaKzzpi3BcfeR89W2ojA6cSj38dLb5u9DjjBWt6SNa8z+oTPW8rjlgXrAi6KVeoaxc9/UkXnchXnnPvmZ2nKSrnHNvercHSjrQOfevUQcHBEGPNvzq11ingxeMjjuMbq4+ckHcIaAnDimx+eaB2+vR5/9WVB0eVI9F4+OtlROGSj7c3Hv2KjVXcQkAJFupLx+SOjI4Lcp00WGLxmhoa/RT0m46ean+/Oq7kb8PUvwc3Y90zn07fcM594aZHSmJhBKAyBTbAXLlp+apf5/yLsxHHxCi1pXQelL9Guu0YFy7Nn2QzIVeG+uraxQqKtf1n1mk9zYXv5/1doncUaa1vIDq4i/Rtf/czoLf4Yt7TCv4uUF0DWlR15CWkrwX/CWUaszMnDe+zcxqJVXecj4AkiGkjpulEweH80JJkOzOrIqTTrJMHpq/MVLOAyNvOnlp4htcUU5BKCZpTdFPlLv0KIn0YgVJMaC5vDuCgEr2h39aq4babTtUmr0px80VMvUYwfj51n8l6Voz+45SHebHSPplpFEhMpU47fUre2+nC3/xhy3LxAJAMVZPHaJfnrSjJiU82VKspCeTMiV9igBQTg5ZOFpHLxsfdxhZ/cuBs+MOAUAOTTlqff7jwtH64KOPdfjiMaUNqAiVeE0cFz8JpTMlHS3pWKX6yW+UdHmUQSF6ldQ0XzZxsJZV0miUKrd12+RIj/hMHto/7hAAIBLn7Tk97hByGtxS2CSIch4tGjaKJqPU6mtrdExCk9T5cOwonp9V3j6WdKn3A8CHmZ2tGtbapFPWTIo7lNgU2py57JA5uvLOZzRuUDJru5QSbULkwqYBVCaSAQhLtVwnr502VE+8+BZTkYGY5Ewomdm1zrn9zOwRZWm7Oue2izQyoIy1NNXrrvWr4g6jLI0f3E9fSnDvaRyqpVGI4Ng2gMrASrVAYU5YOUGHLR6j1jJfiAUoV72NUDrR+3e3UgQCoPwtntCuO556Le4wAACVggE7ZYUBVii1mhoLJZnEtgsUJue6t865F71/n3XOPSvpDUlvZ/ygQnD8RFiu+vQCHTR/VNxhJMKQ/hSJB4BCMV6nvBQ7wGr84L7hBIKKsX6XyZo5sjXy92F0YHZ8LPArbw0lMzta0nmSNmlr7sFJGhdhXCgBVs0BonHzKcvU3rewwqIAAADV7uhl4xO7GmEoktqjn9S4kFh+Vnk7TdI059yrUQcDVAuKbla2CR3hFBRP99juO6czlNcDACDJaB6h0iV15E9S40Ly+UkoPS3pvagDAapJusHEwRu96ejfpGcuXBd3GAAARIppRwBQnvwklNZLutPM7pG0OX2nc+6zkUUFVAmmHaJUzlw7WVfe9UzcYVQcetPLF18dkojtsjCp9hSfHgCUmp+E0r9JulXSI5I+jjYcABWDK+1EOXb5eB27vIJrEQAFYmAEkoDNEGGh9VUYPrfqwvcdHj8JpQ+dc6dEHgmAisDFGaoJ23u0yEsD5cVxmZYYTCP0h0+puvH9F6/Gx2NuM7OjzGyYmbWlfyKPDAAAQCTugKRjCj8AVCc/CaVPyqujJOl+7+e+KIMCAAAAAKAU/I6IverT86MNBCgzeae8OefGliIQAADKDVOyAAAoYwEH1y2eMCiaOBKCZg2CyjlCycyW9PZEM+tvZtPDDwmlxgURAABA/GiTAdUhafXGmLSKQvU2QmlvM7tY0i+Vmub2iqQmSRMkrZA0WtKpkUeIUGUevKhJAQDF4TgKIAwcS1KSdYkNAMgnZ0LJOXeymQ2UtI+kfSUNk7RJ0hOS/s05d3tpQkQkEtZycXTJAQAAVKWiW6XJatYCQNXotYaSc+4NSd/1foDQsaRp5SJFCCBq9EUAycI+CQDVxc8qbwDgG0sHo5pw8RQP+iKAZGGfTA7OSwBKiYQSAAAF4iKqfDHVGgAAoDgklAAAKBD5pPLHqEoAQNJWXUO06FMKT96Ekpk1m9nnzey73u0uM9st+tAAAEA2NISAysRFLVBadCpUOb7+ovkZoXSFpM2SFnq3N0r6cmQRAQBQJuK+9GPKHVAZuKgFkAQ9p4PTgYV8/CSUxjvnLpb0d0lyzm0SuTygKBybASA/jpVAdeHiFYhHz5W36bCCX34SSh+YWR957TozG6/UiCVUCM7d8eFgDQD5caisXkwBKy+FJoSKbQ9xjEC5IXmKSlHn4zHnSvqlpE4zu0rSYkmHRRkUgPLHiRIAUKieveVINr4twB8Obag0eRNKzrmbzOwBSQuUOl+c6Jx7NfLIAJQlTpSoJmzuAAAAqFZ5E0pmNtv79UXv31Fm1irpWefch5FFBgBAwjEQD0CYGN2LYrEJASglP1Pe/lXSbEkPK9UZO937vd3MjnHO3RhhfAAAAEBFY3QvwsY25U/6cyKZCxTGT1HuZyRt75yb45zbQdL2kh6VtFrSxRHGBgAAAABAJMi7AcXxk1Ca7Jx7LH3DOfe4UgmmDdGFhaiQfQcAYCt68QEAqDZcFIfFz5S3P5rZpZJ+6N3eX9KTZtYo6e+RRYZI0X4GgOJxLAUKM3FIv7hDQARckRdpxT4fQHGqbQ80WnJF8zNC6TBJT0k6SdLJkjZ49/1d0opi3tzMTjMzZ2aDvNtmZpeY2VNm9nBGQXAAABKn2hpeQBh+e8YK/fjYRXGHgTAVeU1W7EUdIw2B4rALoVB5Ryg55zZJ+pr309M7hb6xmXVKWiPpLxl37yKpy/uZL+lS719EyDEPDhFguwIAZNPZ1hx3CInFmROoDuzrqBR5RyiZWZeZXWdmj5vZhvRPCO/9DUlnqPv+tKekK13K3ZIGmNmwEN4LWRjdOYgAWxUAAACwLaZYodL4mfJ2hVIjhT5UaorblZJ+UMybmtkekp53zj3U408jJD2XcXujd1+21zjKzO4zs/teeeWVYsIBAAAJxEhHAACA5PJTlLuPc+4WMzPn3LOSvmhmv5V0bm9PMrObJQ3N8qdzJJ0taadsT8tyX9bWpHPuMkmXSdKcOXNocQIAUKEYUQsA/pCIB1BKfhJK75tZjaQ/mdnxkp6X1JHvSc651dnuN7MZksZKeshrII6U9ICZzVNqRFJnxsNHSnrBR4wAAAAAADG1KigScUBh/Ex5O0lSs6TPStpB0sGSDin0DZ1zjzjnOpxzY5xzY5RKIs12zr0k6QZJh3irvS2Q9Dfn3IuFvhcAAACA0uCaHOWGAbBAcfwklMY4595xzm10zh3unNtb0qiI4vm5pA2SnpL0XUmfieh9gFitmpwa5Dd/bHvMkQAAABQnrNEwJKQAoLz4mfK2XtKPfNxXEG+UUvp3J+m4MF4XSLJFEwbpmQvXxR0GAFQtLlyRSFW6XRY7SoTpXUA4ep4bXbUelOBbzoSSme0iaVdJI8zskow/9VdqxTcAyInTD6pBR0tjTO/MHhYWLkORBEy7ARCnnsegSk/S0qkUnt6mvL0g6X5J73v/pn9ukLRz9KEBKEesxoRqsmBcvNNW2dsAAAAKw2VL8XKOUHLOPaTUSmz/6ZxjRFIFI0ELAAAAAKXBqnKoFL1NeXtEXq4h24gD59x20YWFUiAhCwAAkqi+NtVKieuai0s9AFFgRAwqTW9FuXcrWRQAAACA55ojF+i/H3xBA5rrY42Da79gSMRVp0oo3MyAIaAwvU15ezb9u5kNkTTXu3mvc+7lqANDNDhWAgCApOsa0qLTdp4Udxglky6A29a3IeZICsOoi+SJ4zspx0LO5RgzkCS9jVCSJJnZfpK+KunXSnXU/IuZne6cuy7i2BAhTvwAUDyK0AMIQ0NdjS7eezstHB9vof+4MUoEAMpL3oSSpHMkzU2PSjKzwZJulkRCCQAARIZrS1ST/eZ2xh1C2SK3j2JxvgEKU+PnMT2muL3m83kAqhi9jADCwrUiACAKJCO7q4R6WCgtPyOUfmlmv5J0jXd7f0k/jy4kAACAaNFoBgAghVpSKFTehJJz7nQz20vSEqU6CS9zzv0k8sgAAAAiRu80AADVhS6l8ORMKJnZtyRd7Zy70zl3vaTrSxcWAADJRy4CADJwlQagjNCOK15vtZD+JOlrZvaMmV1kZrNKFRRKjJM/AAAACsRFGcpdqWt/cvmFSpEzoeSc+6ZzbqGkZZJel3SFmT1hZl8ws4klixCRYZg/AAAAgGpV6sshLr9QafKu1uace9Y5d5FzbntJn5T0CUlPRB4ZAAAAAMA3VtkFUEp5E0pmVm9mu5vZVZJ+IelJSXtHHhkAAACAqlHo6ouM+tgWnwmAUuitKPcaSQdKWifpXkk/lHSUc+7dEsUGoIzRQYZqwNRhACiecTAFgLKUM6Ek6WxJV0s6zTn3eoniAVDmaBMCAAAA5afnlEmmUCKfnAkl59yKUgYCAAAAoHwVOmUNQLx6dgjTQQy/8tZQAgAA/jTUcVoNEz2jQHlgyhoAVKfepryhEtE6B4DI/O7s1dr80UeRv0/VHcojuFatus8QAJATo+uAwpBQqlLG2g8AELrW5npJ9SV7P0YFFI/PEACqWEznADo14uX4AkLD2HyQkQeAApGKAAAAftGJkSx8H8UjoVTFGKWEKJH5BwAAQRTadOCicCs6igGUElPeAISKRCWq0a4zhurg+aPjDgMAyhIth/CRYwNQCoxQAgCgSIP7NWrRhEFxhwEAsWJwMgBUFxJKAAAUiWsoANWM0TAodyRDU/gcEBQJJQAACkTdDgAAyhdncaA4JJQAAAAAAAAQCAklAAAAAEDVYqYXUBgSSgAAoOpw8QAAYOY6UBwSSgAAFIkilhEpwefKtQRQ/tiPAQRBsy08JJTAhRBCRU8Pqgnbe2nwMQPlodAmJcfS8NCuTzY29WTh+ygeCaUqxskbAAAAxaJJmTysQgqgFEgoAQAAAAAAIBASSgAAAAAAVKlcA9qYQYl8SChVGQ4KABA+x9EVAABUCCZMwi8SSlWKadUAUDwOpQAAVACqmQMFIaEEAAAAAKg6RtcQUJTYEkpmdoKZ/dHMHjOzizPuX29mT3l/2zmu+AAUh44eAACqw9Th/TWqrVlnrp1c1OsU3HYgJwAAsaiL403NbIWkPSVt55zbbGYd3v1TJR0gaZqk4ZJuNrOJzrmP4ogTQHC06YDoka8FkCTNDXX6vzNWFPx8SjEAQHmKa4TSsZIudM5tliTn3Mve/XtK+qFzbrNz7s+SnpI0L6YYAQBINK7BCscoSgCViENbspE8RaWJK6E0UdKOZnaPmf3GzOZ694+Q9FzG4zZ69yFCvTWqz1g7SfPGtpUuGAAoQyQnyhiNewAVqJBD26B+DaHHASQR7bbwRDblzcxuljQ0y5/O8d53oKQFkuZKutbMxin7sS/r121mR0k6SpJGjRoVRshVx8+J5jPLJ+gzyydEHgsAlCW6GgEAFeKmk5fpjfc+iDsMxMhVWaaFZlzxIksoOedW5/qbmR0r6XqX2mLvNbOPJQ1SakRSZ8ZDR0p6IcfrXybpMkmaM2dOdW35AABUAcfkDQAomYF9GzSwL6OUqpGRWUGB4pry9lNJKyXJzCZKapD0qqQbJB1gZo1mNlZSl6R7Y4oRAAAkAA1dAEAUBvatlyS1NpNIAwoRyypvkr4v6ftm9qikDyQd6o1WeszMrpX0uKQPJR3HCm8AEL1+jXUaObBP3GEAAACUzEHzR6uxrkZ7zx4ZdyhAWYoloeSc+0DSwTn+dr6k80sbEVDdDl88RjNGtIbyWqPbmyVJwwY0hfJ6KI1Hv7Rz3CGUpUlDWiRJ248aGHMkAFC+OlqaJL2lxvrCJk8whhGFqq0x7T+XerxAoeIaoQQgQc7dfVpor3XwgtEaN7ifFo1vD+01gaSaN7ZNt5+5QiMGMLoLAAr1jf1m6aYn/qqJXpIeKCdhdcoC5YiEEoBQmZkWTxgUdxhAyYwc2Bx3CABQ1lqb67XPDkw5Ksbes0dq6cRBevFv78cdSlXZcMGurBSGqkZCCRXp5lOW6plX34s7DABInB8ds1A1NH4BoKJ8bb+ZkqT7n31DkrSki869UqjhhIoqR0KpyqRXyrGM32viWusvQhM6WhlxJIwAAA9dSURBVDShg2HTQKX4zsE7aPOHrNEQhrlj2uIOIRGcXNwhAEDodhg9UE9+eRc11FVgA78CsGopKg0JpSpz1NJxev3dzfrUkrFqqKvRoQtH6/iVXXGHpUH9GuMOAUCCrZ0+NO4QUKGMcr4AKgzJJBSqWrpaahlZFhoSSlWmX2OdvvwPM7bc/tKe02OMJrUz//O+MzV/LD3mAAAAAFBq1ZZe+eFRC/TT3z+v1j71cYdS9khfI3b77DBSnW0UtQUAAACQfLvPHB53CJE6dvl4SVJHS2XOIpk4pEVnrJ3MFMQQMEIJAACUlekjWvXzR17S8AFNcYeCCuWqZd5HTEa305GI8vX4eTursa427jAitf/cUdp/7qi4wyiZ9btM1vQRrXGHUZZIKAEAgERK9xz2HJJ+zNLxWjZxsKYNp/GHaNF5Hb4nzlsb+oIwjDJAKTU3cAldaY5eNj7uEMoWU96ACrZqcgdzgwGUrdY+9Tpvz2m66tPzu91fU2Mkk4Ay1aehtuJHd1Sbf953psYN7qv6WhJ7QLUhvQpUsO8dNjfuEACgKIcsHBN3CACAXuw5a4T2nDUi7jAAxIARSgAAAAAAAAiEEUoAAJSZGq9eSJ8Gpo0AAPy75dRlevXtzXGHAaBCkFACAKDMzB41QCet7tIn51fPCiwAkE9b34a4Q0i88YP7afzgfnGHgYTp15RKC6yZOqTk7/2VvWbotXdIcpYrEkoAAJQZM9NJqyfGHUZZK2RZ+F+cuKPe2fxh+MEACMX1xy6KOwSgLPVvqte956xSW3Ppk7IHzqNzrJyRUAIAAFUryGrjU4b1jy4QAEVjZVugcB0tTXGHgDJEQgkAAABAXqPamvX6ux/EHcY2giSGAaBQhy0aoynDWuIOI1FIKAEAAADI69enLY87BACIzRf3mBZ3CIlDQgkAAABAXjU1DAUCAGxFQgkAACBEPzhiHrVcAABAxSOhBAAAEKIduwbHHQIS6qK9Z2hwS2PcYQAAEAoSSgAAAEAJ7D+X5bEBSKfvPCnuEIBQkFACAAAAAKAEnrlwXdwhAKGpiTs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“text/plain”: [

“<Figure size 1440x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plot_analog_stream_channel(electrode_stream, 0, from_in_s=0, to_in_s=500)”

]

}, {

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

“For the purposes of this tutorial, we are going to work with the channel with index 9 and ID 171. In the MEA well, this channel was at position '43'. We chose this channel because it exhibits nice multi-unit activity which is perfect for a tutorial on spike sorting. Looking at a short time segment for this channel, we can clearly see spikes of vastly different amplitudes which most likely stem from different neurons.”

]

}, {

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

{

“data”: {

“image/png”: 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“text/plain”: [

“<Figure size 1440x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plot_analog_stream_channel(electrode_stream, 9, from_in_s=155, to_in_s=200)”

]

}, {

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

“Depending on the bandwidth of the recording, it can be necessary to perform a bandpass filtering in order to remove low-frequency fluctuations. A bandpass filter with cutoffs [100, 3500] Hz had been applied during the recording of this dataset, so no further filtering is necessary here.”

]

}, {

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

{

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

“Bandwidth: 100 - 3500 Hzn”

]

}

], “source”: [

“channel_id = 171n”, “info = electrode_stream.channel_infos[channel_id].infon”, “print(“Bandwidth: %s - %s Hz” % (info[‘HighPassFilterCutOffFrequency’], info[‘LowPassFilterCutOffFrequency’]))n”, “n”, “signal = electrode_stream.get_channel_in_range(channel_id, 0, electrode_stream.channel_data.shape[1])[0]”

]

}, {

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

“## Spike Detectionn”, “n”, “Our first task is going to be to detect spikes on the data channel and extract cutouts of the spike waveforms. n”, “n”, “Usually, the easiest way to get these spike waveforms would be to perform the spike detection in the MCS software, export them together with the recorded raw data to HDF5 and then simply access them with the McsPy tools. These spike waveforms would be stored as a SegmentStream (see the basic toolbox tutorial for more information on interacting with ``SegmentStream``s).n”, “n”, “However, for tutorial purposes, let’s say we want to redo the spike detection in Python. We use a very simple threshold-based spike detection here.”

]

}, {

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

“First, we need to determine a suitable threshold for spike detection. A reasonable way to do this is to estimate the noise level of the channel, and then use a multiple of the noise level as the threshold (a multiplication factor of 5 tends to work quite well). While we could estimate the noise level from the standard deviation of the signal, this has the drawback that the spikes in the signal can have a strong influence on the standard deviation and lead to an unnecessarily high threshold. The MAD estimator is more robust against outlier and might therefore be more appropriate:”

]

}, {

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

{

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

“Noise Estimate by Standard Deviation: 8.89075e-06 Vn”, “Noise Estimate by MAD Estimator : 5.83227e-06 Vn”

]

}

], “source”: [

“noise_std = np.std(signal)n”, “noise_mad = np.median(np.absolute(signal)) / 0.6745n”, “print(‘Noise Estimate by Standard Deviation: {0:g} V’.format(noise_std))n”, “print(‘Noise Estimate by MAD Estimator : {0:g} V’.format(noise_mad))”

]

}, {

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

“As you can see, the difference between the two is about 3 µV, so we’ll stick with the MAD estimator. We are using a negative threshold here, so we are just looking at the negative peak of each spike.”

]

}, {

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

“spike_threshold = -5 * noise_mad # roughly -30 µV”

]

}, {

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

“Let’s plot the same data section as above together with the spike threshold. We are detecting some very small spikes with this threshold, so depending on your aim you may want to increase the multiplication factor.”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “metadata”: {

“scrolled”: false

}, “outputs”: [

{

“data”: {

“image/png”: 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“text/plain”: [

“<Figure size 1440x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“plot_analog_stream_channel(electrode_stream, 9, from_in_s=155, to_in_s=200, show=False)n”, “_ = plt.plot([155, 200], [spike_threshold*1e6, spike_threshold*1e6]) # converts the threshold to µV for plottingn”, “plt.show()”

]

}, {

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

“Our spike detector needs to take into account that a spike typically comprises multiple samples, so we can’t simply take each sample that exceeds the threshold as an individual spike detection. Instead, we’ll define a dead time, meaning that whenever we detect a spike, the next few samples within the dead time won’t trigger a spike detection by themselves. n”, “n”, “In order to reduce the detection jitter, we additionally search for each spike the minimum in the signal for a short period of time after the threshold crossing. This will be the timestamp for each spike. We’ll define some helper functions to achieve this:”

]

}, {

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

“def detect_threshold_crossings(signal, fs, threshold, dead_time):n”, ” “””n”, ” Detect threshold crossings in a signal with dead time and return them as an arrayn”, ” n”, ” The signal transitions from a sample above the threshold to a sample below the threshold for a detection andn”, ” the last detection has to be more than dead_time apart from the current one.n”, ” n”, ” :param signal: The signal as a 1-dimensional numpy arrayn”, ” :param fs: The sampling frequency in Hzn”, ” :param threshold: The threshold for the signaln”, ” :param dead_time: The dead time in seconds. n”, ” “””n”, ” dead_time_idx = dead_time * fsn”, ” threshold_crossings = np.diff((signal <= threshold).astype(int) > 0).nonzero()[0]n”, ” distance_sufficient = np.insert(np.diff(threshold_crossings) >= dead_time_idx, 0, True)n”, ” while not np.all(distance_sufficient):n”, ” # repeatedly remove all threshold crossings that violate the dead_timen”, ” threshold_crossings = threshold_crossings[distance_sufficient]n”, ” distance_sufficient = np.insert(np.diff(threshold_crossings) >= dead_time_idx, 0, True)n”, ” return threshold_crossingsn”, “n”, “def get_next_minimum(signal, index, max_samples_to_search):n”, ” “””n”, ” Returns the index of the next minimum in the signal after an indexn”, ” n”, ” :param signal: The signal as a 1-dimensional numpy arrayn”, ” :param index: The scalar index n”, ” :param max_samples_to_search: The number of samples to search for a minimum after the indexn”, ” “””n”, ” search_end_idx = min(index + max_samples_to_search, signal.shape[0])n”, ” min_idx = np.argmin(signal[index:search_end_idx])n”, ” return index + min_idxn”, “n”, “def align_to_minimum(signal, fs, threshold_crossings, search_range):n”, ” “””n”, ” Returns the index of the next negative spike peak for all threshold crossingsn”, ” n”, ” :param signal: The signal as a 1-dimensional numpy arrayn”, ” :param fs: The sampling frequency in Hzn”, ” :param threshold_crossings: The array of indices where the signal crossed the detection thresholdn”, ” :param search_range: The maximum duration in seconds to search for the minimum after each crossingn”, ” “””n”, ” search_end = int(search_range*fs)n”, ” aligned_spikes = [get_next_minimum(signal, t, search_end) for t in threshold_crossings]n”, ” return np.array(aligned_spikes)”

]

}, {

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

“Now, let’s perform the spike detection using our detector with dead time. Then, we align the spikes to their negative peaks.”

]

}, {

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

“fs = int(electrode_stream.channel_infos[channel_id].sampling_frequency.magnitude)n”, “crossings = detect_threshold_crossings(signal, fs, spike_threshold, 0.003) # dead time of 3 msn”, “spks = align_to_minimum(signal, fs, crossings, 0.002) # search range 2 ms”

]

}, {

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

“To double check, let’s plot the signal together with the detected spikes:”

]

}, {

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

{

“data”: {

“image/png”: 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“text/plain”: [

“<Figure size 1440x432 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“timestamps = spks / fsn”, “range_in_s = (155, 200)n”, “spikes_in_range = timestamps[(timestamps >= range_in_s[0]) & (timestamps <= range_in_s[1])]n”, “n”, “plot_analog_stream_channel(electrode_stream, 9, from_in_s=range_in_s[0], to_in_s=range_in_s[1], show=False)n”, “_ = plt.plot(spikes_in_range, [spike_threshold*1e6]*spikes_in_range.shape[0], ‘ro’, ms=2)n”, “plt.show()”

]

}, {

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

“## Spike Waveformsn”, “n”, “Having found the spike timestamps, we may now extract the spike waveforms. For this, we simply cut out a portion of the signal around each spike. Spikes too close to the start or end of the signal that a full cutout is not possible are ignored.”

]

}, {

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

“def extract_waveforms(signal, fs, spikes_idx, pre, post):n”, ” “””n”, ” Extract spike waveforms as signal cutouts around each spike index as a spikes x samples numpy arrayn”, ” n”, ” :param signal: The signal as a 1-dimensional numpy arrayn”, ” :param fs: The sampling frequency in Hzn”, ” :param spikes_idx: The sample index of all spikes as a 1-dim numpy arrayn”, ” :param pre: The duration of the cutout before the spike in secondsn”, ” :param post: The duration of the cutout after the spike in secondsn”, ” “””n”, ” cutouts = []n”, ” pre_idx = int(pre * fs)n”, ” post_idx = int(post * fs)n”, ” for index in spikes_idx:n”, ” if index-pre_idx >= 0 and index+post_idx <= signal.shape[0]:n”, ” cutout = signal[(index-pre_idx):(index+post_idx)]n”, ” cutouts.append(cutout)n”, ” return np.stack(cutouts)”

]

}, {

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

{

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

“Cutout array shape: (2579, 75)n”

]

}

], “source”: [

“pre = 0.001 # 1 msn”, “post= 0.002 # 2 msn”, “cutouts = extract_waveforms(signal, fs, spks, pre, post)n”, “print(“Cutout array shape: ” + str(cutouts.shape)) # number of spikes x number of samples”

]

}, {

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

“Let’s plot an overlay of the extracted cutouts:”

]

}, {

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

“def plot_waveforms(cutouts, fs, pre, post, n=100, color=’k’, show=True):n”, ” “””n”, ” Plot an overlay of spike cutoutsn”, ” n”, ” :param cutouts: A spikes x samples array of cutoutsn”, ” :param fs: The sampling frequency in Hzn”, ” :param pre: The duration of the cutout before the spike in secondsn”, ” :param post: The duration of the cutout after the spike in secondsn”, ” :param n: The number of cutouts to plot, or None to plot all. Default: 100n”, ” :param color: The line color as a pyplot line/marker style. Default: ‘k’=blackn”, ” :param show: Set this to False to disable showing the plot. Default: Truen”, ” “””n”, ” if n is None:n”, ” n = cutouts.shape[0]n”, ” n = min(n, cutouts.shape[0])n”, ” time_in_us = np.arange(-pre*1000, post*1000, 1e3/fs)n”, ” if show:n”, ” _ = plt.figure(figsize=(12,6))n”, ” n”, ” for i in range(n):n”, ” _ = plt.plot(time_in_us, cutouts[i,]*1e6, color, linewidth=1, alpha=0.3)n”, ” _ = plt.xlabel(‘Time (%s)’ % ureg.ms)n”, ” _ = plt.ylabel(‘Voltage (%s)’ % ureg.uV)n”, ” _ = plt.title(‘Cutouts’)n”, ” n”, ” if show:n”, ” plt.show()”

]

}, {

“cell_type”: “code”, “execution_count”: 17, “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”: [

“plot_waveforms(cutouts, fs, pre, post, n=250)”

]

}, {

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

“From this waveform overlay it seems clear that the spikes we are seeing stem from (at least) two different neurons. In order to determine, which spikes stem from the same neuron, we need to analyze these cutouts further. “

]

}, {

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

“## Feature Extractionn”, “n”, “We would expect that we should be able to distinguish the spikes from different neurons based on the shape of the spike waveform. So we now need a way to find a good representation of the waveform shape. “Good” means in this case that the result should be well separable, i.e. that similar waveforms are represented close to each other and far away from dissimilar waveforms. In addition, the representation should have as few dimensions as possible because the clustering step tends to work better if there aren’t too many dimensions involved.n”, “n”, “This is commonly called the Feature Extraction step.”

]

}, {

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

“We could try to manually design some features that seem useful. For example, we could take the minimum and maximum amplitude and try to work with those:”

]

}, {

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

{

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 576x576 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“min_amplitude = np.amin(cutouts, axis=1)n”, “max_amplitude = np.amax(cutouts, axis=1)n”, “n”, “_ = plt.figure(figsize=(8,8))n”, “_ = plt.plot(min_amplitude*1e6, max_amplitude*1e6,’.’)n”, “_ = plt.xlabel(‘Min. Amplitude (%s)’ % ureg.uV)n”, “_ = plt.ylabel(‘Max. Amplitude (%s)’ % ureg.uV)n”, “_ = plt.title(‘Min/Max Spike Amplitudes’)n”, “n”, “plt.show()”

]

}, {

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

“We can directly see some distinct clusters in this plot which might represent spikes from different neuronal sources, but a lot of spikes are closely bunched together in the lower right corner. Taking only the minimum and maximum amplitude might be a bit too simplistic, because for example judging from the cutout overlay plot, the maximum can happen before or after time point 0 and with similar values. Using the amplitude alone won’t differentiate between these different shapes. n”, “n”, “More sophisticated feature design could remedy this, but there are also more general feature extraction methods available that require less manual interaction. A widespread method for this is principal component analysis ([PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)). It finds a linear transformation of the data into principal component space, thereby decorrelating the data. Because each principle component (PC) explains as much variance in the data as possible (while being orthogonal to all previous principal components), we can use the explained variance as a measure of the “importance” of each PC. This will help us decide, how many dimensions (= PCs) we need to keep.”

]

}, {

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

{

“data”: {

“text/plain”: [

“PCA(copy=True, iterated_power=’auto’, n_components=None, random_state=None,n”, ” svd_solver=’auto’, tol=0.0, whiten=False)”

]

}, “execution_count”: 19, “metadata”: {}, “output_type”: “execute_result”

}, {

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

“[4.95861981e-01 1.86792909e-01 1.07960458e-01 4.92467665e-02n”, ” 3.92615578e-02 2.43178104e-02 1.54772580e-02 1.42838769e-02n”, ” 1.32672412e-02 9.07951432e-03 6.22540386e-03 5.76332370e-03n”, ” 5.09706162e-03 4.57872079e-03 3.37948805e-03 2.88224864e-03n”, ” 2.35134998e-03 2.00488981e-03 1.77034798e-03 1.47102577e-03n”, ” 1.18203490e-03 1.06396539e-03 9.34307355e-04 7.84827463e-04n”, ” 7.03255677e-04 6.52196920e-04 5.41354290e-04 4.90419984e-04n”, ” 4.14199911e-04 3.63004816e-04 3.04959856e-04 2.61099008e-04n”, ” 2.01444988e-04 1.66950952e-04 1.47071914e-04 1.26653755e-04n”, ” 1.01048377e-04 8.93055456e-05 7.27223316e-05 6.06567333e-05n”, ” 4.77039212e-05 3.97085398e-05 3.42875628e-05 2.63952644e-05n”, ” 2.20130932e-05 1.76933484e-05 1.45767003e-05 1.18268421e-05n”, ” 9.58967283e-06 7.92411460e-06 6.38193314e-06 4.95505369e-06n”, ” 4.30927163e-06 3.46774565e-06 2.74747862e-06 2.31670474e-06n”, ” 1.79492225e-06 1.49837614e-06 1.21960289e-06 9.88322222e-07n”, ” 8.03217275e-07 6.44498157e-07 5.51653989e-07 3.94522000e-07n”, ” 3.29783060e-07 2.69825823e-07 2.18465142e-07 1.66291769e-07n”, ” 1.33458366e-07 1.05186644e-07 8.99273536e-08 6.69644259e-08n”, ” 6.31986343e-08 3.59873090e-08 1.45556845e-08]n”

]

}

], “source”: [

“scaler = StandardScaler()n”, “scaled_cutouts = scaler.fit_transform(cutouts)n”, “n”, “pca = PCA()n”, “pca.fit(scaled_cutouts)n”, “print(pca.explained_variance_ratio_)”

]

}, {

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

“Thus, the first 2 PCs together explain 49.6 % + 18.7 % = 67.3 % of the variance in the data. This might be enough, so we will now project our cutouts on the first 2 PCs, giving us only 2 coefficients for each spike: The weight of the 1st and the 2nd PC. These coefficients are dimensionless.”

]

}, {

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

{

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 576x576 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“pca.n_components = 2n”, “transformed = pca.fit_transform(scaled_cutouts)n”, “n”, “_ = plt.figure(figsize=(8,8))n”, “_ = plt.plot(transformed[:,0], transformed[:,1],’.’)n”, “_ = plt.xlabel(‘Principal Component 1’)n”, “_ = plt.ylabel(‘Principal Component 2’)n”, “_ = plt.title(‘PC1 vs PC2’)n”, “n”, “plt.show()”

]

}, {

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

“Here we can see 4 quite well defined clusters in the dataset. Out of curiosity, we can also test what happens if we take the first 3 PCs instead of 2 (although this makes the visualization a bit more tricky):”

]

}, {

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

{

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 1080x360 with 3 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“pca.n_components = 3n”, “transformed_3d = pca.fit_transform(scaled_cutouts)n”, “n”, “_ = plt.figure(figsize=(15,5))n”, “_ = plt.subplot(1, 3, 1)n”, “_ = plt.plot(transformed_3d[:,0], transformed_3d[:,1],’.’)n”, “_ = plt.xlabel(‘Principal Component 1’)n”, “_ = plt.ylabel(‘Principal Component 2’)n”, “_ = plt.title(‘PC1 vs PC2’)n”, “_ = plt.subplot(1, 3, 2)n”, “_ = plt.plot(transformed_3d[:,0], transformed_3d[:,2],’.’)n”, “_ = plt.xlabel(‘Principal Component 1’)n”, “_ = plt.ylabel(‘Principal Component 3’)n”, “_ = plt.title(‘PC1 vs PC3’)n”, “_ = plt.subplot(1, 3, 3)n”, “_ = plt.plot(transformed_3d[:,1], transformed_3d[:,2],’.’)n”, “_ = plt.xlabel(‘Principal Component 2’)n”, “_ = plt.ylabel(‘Principal Component 3’)n”, “_ = plt.title(‘PC2 vs PC3’)n”, “plt.show()”

]

}, {

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

“## Clusteringn”, “n”, “Now to the final step: Finding clusters in the feature space and assigning each spike to its closest cluster. There are [many, many](https://scikit-learn.org/stable/modules/clustering.html) clustering algorithms out there, but we will use a very simple one: A Gaussian Mixture Model ([GMM](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture)). Of course, GMMs have their drawbacks (number of clusters must be specified, many parameters to fit, don’t scale very well), but for our case they might be sufficient.n”, “n”, “Judging from our plot, 4 clusters might be sufficient as a description of the data. The initialization of a GMM can have a lot of influence on the output, so it is possible that you need to fit the GMM multiple times in order to find a good fit.”

]

}, {

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

{

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 576x576 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“n_components = 4n”, “gmm = GaussianMixture(n_components=n_components, n_init=10)n”, “labels = gmm.fit_predict(transformed)n”, “n”, “_ = plt.figure(figsize=(8,8))n”, “for i in range(n_components):n”, ” idx = labels == in”, ” _ = plt.plot(transformed[idx,0], transformed[idx,1],’.’)n”, ” _ = plt.title(‘Cluster assignments by a GMM’)n”, ” _ = plt.xlabel(‘Principal Component 1’)n”, ” _ = plt.ylabel(‘Principal Component 2’)n”, ” _ = plt.axis(‘tight’)n”, “plt.show()”

]

}, {

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

“Once we’ve found a sensible clustering, we can now go back and check the waveforms in the different clusters”

]

}, {

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

{

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 576x576 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“_ = plt.figure(figsize=(8,8))n”, “for i in range(n_components):n”, ” idx = labels == in”, ” color = plt.rcParams[‘axes.prop_cycle’].by_key()[‘color’][i]n”, ” plot_waveforms(cutouts[idx,:], fs, pre, post, n=100, color=color, show=False)n”, “plt.show()”

]

}, {

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

“This concludes this short tutorial on neuronal data analysis with the McsPyDataTools package.”

]

}

], “metadata”: {

“kernelspec”: {

“display_name”: “Python 3”, “language”: “python”, “name”: “python3”

}, “language_info”: {

“codemirror_mode”: {

“name”: “ipython”, “version”: 3

}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.7.0”

}

}, “nbformat”: 4, “nbformat_minor”: 2

}

Authors

Alphabetical list of contributors to the McsPyDataTools toolbox:

• Janko Dietzsch <dietzsch@multichannelsystems.com>
• Florian Helmhold <helmhold@multichannelsystems.com>
• Hans Loeffler <loeffler@multichannelsystems.com>
• Armin Walter <walter@multichannelsystems.com>
• Ole Wenzel <wenzel@multichannelsystems.com>

The McsPyDataTools toolbox is maintained by Armin Walter (Multi Channel Systems MCS GmbH) <walter@multichannelsystems.com>.

History

Version 0.4.2 (2022-02-11)

• Allowed reading of data files with InfoChannel version 2

Version 0.4.1 (2020-04-01)

• Bugfix for the get_channel_sample_timestamps function in McsData.py

Version 0.4.0 (2019-12-18)

• Improved documentation
• Improved support for CMOS-MEA files
• New Jupyter Notebook Tutorials

Version 0.3.0 (2018-06-13)

• Migrated to Python 3
• Analog streams with Acceleration, Gyroscope and OptoStim data supported
• New native HDF5-based CMOS-MEA format supported
• Jupyter notebooks added and extended to demonstrate usage patterns

Version 0.2.2 (2017-09-19)

• Bugfix Supported Protocol-Version for SegmentEntityInfo

Version 0.2.1 (2016-08-12)

• Bugfix CMOSSpike class

Version 0.2 (2015-04-29)

• support for average segments added
• improved documentation (Sphinx-based)

Version 0.1 (2014-03-27)

• first release

Indices and tables

• Index
• Module Index
• Search Page