Default Nodes

code

CodeNode

Custom python coding node.

Input Terminals

arguments (list of strings):

comma separated list of terminal names which dynamically create input terminals

Output Terminals

functionOut (python function):

returns the function which consists of the custom code

returnOut (any):

the value of returnOut

general

BooleanComparatorNode

Compares two inputs

Input Terminals

a (object):

a single input object of any type

b (object):

a single input object of any type

Output Terminals

out (bool):

a single output boolean that is the result of the operation selected in the operation terminal

User Terminals

operation:

a user selection that determines the comparison to be made

BrowseDirectoryNode

Browse to a directory and return the path as a string to that directory.

Output Terminals

directory (str):

Path to the directory. If the directory at the path does not exist, returns the string ‘Does Not Exist’.

BrowseFileNode

Browse to a File and return the path as a string to that file.

Output Terminals

file (str):

Path to the file. If the file at the path does not exist, returns the string ‘Does Not Exist’.

DisplayNode

Show the value of the input connection

Input Terminals

input (any):

Any object to display.

GlobFileNode

Use glob wild cards to match files or directories.

Output Terminals

matches (list):

List of paths to the files matched with glob. If no matches, returns an empty list.

PathJoin

This node will join two path elements

Input Terminals

path1 (str):

The first part of the path to join

path2 (str):

the secont part of the path to join

Output Terminals

output (str):

the joined path

PeriodTriggerNode

Checks if a monotonically increasing float pass a defined period and returns a True trigger whenever the float passes the trigger period. Useful for limiting a task only to equally spaced values.

Input Terminals

Value (float):

a single input float that is expected to increase monotonically

Period (float):

the period between desired event triggers

Output Terminals

Trigger (bool):

a single output boolean which can be used to trigger other events

Delta (float):

The true period between the last trigger and the current.

PickleObjectsNode

Uses pickle to save and load objects to file

Input Terminals

In (serializable object):

an input object that is able to be serialized and saved

filepath (str):

a string which is the filepath for the file to be saved or loaded

Output Terminals

Out (python object):

an object that was deserialized from file

User Terminals

Pickler (str):

a string that defines the type of pickler to use on the object

mode (str):

a string that switches between saving and loading of the object

PrintNode

Print an object to stout (i.e. print(object))

Input Terminals

object (object):

An object to print.

PrintTypeNode

Print the type of an object to stout (i.e. print(type(object))).

Input Terminals

object (object):

An object to print the type of.

SaveNode

Attempts to save the object to a text file.

Input Terminals

object (int, float, str, bool, list, tuple, list, tuple):

The object to be saved to the text file. Can be a int, float, str, bool, list, tuple, list, tuple, list(list), tuple(tuple), list(tuple), or tuple(list).

file (str):

The path to an existing or new file to save to.

method (str):

A string that determines the method of saving to an existing file, either ‘overwrite’ to replace the existing file or ‘append’ to append to the existing file.

format (str):

A string selection the text file format. Either ‘csv’ to write a deliminated text file or ‘json’ to write a json text file.

delimiter (str):

A string used to separate values if the format is set to ‘csv’.

SliceNode

This node will slice an iterable

Input Terminals

iterable (object):

An iterable object that you wish to slice

slice (str):

a string descrbing a slice of the iterable (i.e. [2:5, 4])

Output Terminals

output (object):

the resulting slice

StopNode

This node will stop the loop that it is placed inside.

Input Terminals

stop (bool):

Set the value to True to emit a stop signal to the loop. If you want the loop to continue, this value should be False (default)

StringJoin

This node will join two path elements

Input Terminals

path1 (str):

The first part of the path to join

path2 (str):

the secont part of the path to join

Output Terminals

output (str):

the joined path

TableView

Display 1D or 2D data in a table.

Input Terminals

table (dict, list, tuple, np.ndarray, pd.DataFrame):

An object to display as a 2D table.

TestOutput

Node dedicated to simplifying test harness testing. Attach to desired output and reference in the test method.

imageio

GetFrameNode

Get a specified frame from the video file.

Inputs

video (imageio.plugins.ffmpeg.Reader):

the imageio.plugins.ffmpeg.Reader video reader object

index (int):

the frame number to get

Output Terminals

frame (ndarray):

the frame as a numpy.ndarray

ReadImageNode

Read an image file.

Inputs

path (str):

a path to the image file

Output Terminals

image (ndarray):

a numpy.ndarray

ReadVideoNode

Read an video file.

Inputs

path (str):

a path to the image file

Output Terminals

duration (float):

length of the video file in seconds

frames (int):

number of frames in video file

fps (float):

frames per second

size (tuple):

a tuple of the width and height of the frame

video (imageio.plugins.ffmpeg.Reader):

a imageio.plugins.ffmpeg.Reader object that can be used to access individual frames from the video

math

AddNode

Sum the inputs

Input Terminals

input (numerical):

multi input of numeric values to be summed

Output Terminals

result (numerical):

the value of those summed inputs

DivideNode

Divide the inputs

Input Terminals

numerator (numerical):

number to be divided

denominator (numerical):

number to divide by

Output Terminals

result (numerical):

the value of those divided inputs

FactorialNode

calculate the factorial of a number.

Input Terminals

input (numerical):

number to calculate the factorial of

Output Terminals

result (numerical):

the resulting factorial

MultiplyNode

Multiply the inputs

Input Terminals

input (numerical):

multi input of numeric values to be multiplied

Output Terminals

result (numerical):

the value of those multiplied inputs

PowerNode

Raise base to a power

Input Terminals

base (numerical):

number to be multiplied

exponent (numerical):

the power to be raised

Output Terminals

result (numerical):

the value of those multiplied inputs

RootNode

nth root of a number

Input Terminals

radicand (numerical):

number to be reduced

degree (numerical):

number of degrees to reduce

Output Terminals

result (numerical):

the resulting base

SubtractNode

Subtract the inputs

Input Terminals

minuend (numerical):

number to be subtracted from

subtrahend (numerical):

number to subtract

Output Terminals

result (numerical):

the value of those subtracted inputs

matplotlib

BasePlotNode

base node for all the matplotlib nodes, not meant to be used directly

ImageNode

Display an image

Input Terminals

image (np.ndarray):

a 2D or 3D array

MultiPlotNode

multiple x, y plots

Input Terminals

x (iterable):

a 1D iterable (list, np.ndarray, etc.)

y (iterable):

a 1D iterable (list, np.ndarray, etc.)

PairNode

x, y plot

Input Terminals

x (iterable):

a 1D iterable (list, np.ndarray, etc.)

y (iterable):

a 1D iterable (list, np.ndarray, etc.)

PlotNode

x, y plot

Input Terminals

x (iterable):

a 1D iterable (list, np.ndarray, etc.)

y (iterable):

a 1D iterable (list, np.ndarray, etc.)

numpy

ABSNode

Calculate the absolute value of the input array

Input Terminals

array (iterable):

an iterable to calculate the FFT of

Output Terminals

abs(array) (np.array):

the absolute value of the array

ArrayNode

Create an array

Input Terminals

shape (str):

a string of comma seporated integers defining the array extents

type (str):

a string selecting the data format of the array

value (float):

an initial value to fill the array with

Output Terminals

array (np.ndarray):

the resulting array

AsArrayNode

Create an array from an iterable

Input Terminals

input (iterable):

an iterable that can be converted to an array

Output Terminals

array (np.ndarray):

the resulting array

ConditionalIndexNode

Create an array of zeros with he same shape as the inout array

Input Terminals

array (np.array):

an array to copy the shape of

Output Terminals

zeros (np.array):

an array with the same shape, but filled with zeros

DivideNode

Divide numpy arrays that have the same shape, connection order matters

Input Terminals

array (np.ndarray):

array to divide

Output Terminals

result (np.ndarray):

the resulting array

FFTNode

Calculate the FFT of the input array

Input Terminals

array (iterable):

an iterable to calculate the FFT of

rate(float):

sample rate to determine the freuency range (Hz)

Output Terminals

frequency (np.array):

the calculated frequency range (Hz)

fft (np.array):

the fft

HistogramNode

Compute the histogram of a set of data

Input Terminals

array (np.ndarray):

Input data. The histogram is computed over the flattened array.

bins (int):

the number of bins

Output Terminals

hist (np.ndarray):

the values of the histogram

edges(np.ndarray):

the bin edges (length(hist)+1)

centers (np.ndarray):

the bin centers

IndexNode

Indexes into an array. Click the plus or minus button to add dimension arguments

Input Terminals

array (np.ndarray):

the input array to be indexed

assign (np.ndarray):

an array, float, or int to assign to the indexed region. Note: an array needs to have the same dimensions as the index.

Output Terminals

array (np.ndarray):

the indexed array

array (np.ndarray):

the input array with the assignment if provided

LoadTXT

Loads an array from a text file

Input Terminals

file (str):

path to the file of interest

skiprows (str):

skip the first skiprows lines; default: 0.

Output Terminals

array (numpy.array):

the array read from the file

MaxNode

Calculate the max of the input array

Input Terminals

array (iterable):

an iterable to calculate the max of

Output Terminals

max (float):

the calculated max

MeanNode

Calculate the mean of the input array

Input Terminals

array (iterable):

an iterable to calculate the mean of

Output Terminals

mean (float):

the calculated mean

MinNode

Calculate the min of the input array

Input Terminals

array (iterable):

an iterable to calculate the min of

Output Terminals

min (float):

the calculated min

MultiplyNode

Multiply numpy arrays that have the same shape

Input Terminals

array (np.ndarray):

array to multiply

Output Terminals

result (np.ndarray):

the resulting array

NormalizeNode

Normalize an array so the minimum is 0 and the maximum is 1.

Input Terminals

array (iterable):

an iterable to calculate the Normalize array of

Output Terminals

noramlized (numpy.array):

the calculated variance

RandomNode

Generate an array of random values

Input Terminals

shape (str):

a string of comma seporated integer values (i.e. 3,6,3,4) defining the shape of the array

Output Terminals

array (np.ndarray):

the randomly generated array

RangeNode

Generate an array of floats given a starting value, ending value, and a step.

Input Terminals

start (float):

the starting value

stop (float):

the ending value

step (float):

the step between values

type (str):

the dtype of the resulting array

Output Terminals

range (np.ndarray):

the resulting range

StdNode

Calculate the std of the input array

Input Terminals

array (iterable):

an iterable to calculate the std of

Output Terminals

std (float):

the calculated std

SubtractNode

Subtract numpy arrays that have the same shape, connection order matters.

Input Terminals

array (np.ndarray):

array to subtract

Output Terminals

result (np.ndarray):

the resulting array

SumNode

Add numpy arrays that have the same shape

Input Terminals

array (np.ndarray):

array to add

Output Terminals

result (np.ndarray):

the resulting array

TransposeNode

Transpose the input array

Input Terminals

input (np.ndarray):

the input array

Output Terminals

array (np.ndarray):

the resulting array

TrigNode

Compute the selected trig function of the input one dimensional array

Input Terminals

input (np.ndarray):

the input one dimensional array

operation (str):

trig function

Output Terminals

array (np.ndarray):

the resulting array

VStackNode

Combine (stack) arrays in the vertically

Input Terminals

transpose (bool):

transpose the resulting array

Output Terminals

array (np.ndarray):

the resulting array

VarNode

Calculate the variance of the input array

Input Terminals

array (iterable):

an iterable to calculate the variance of

Output Terminals

var (float):

the calculated variance

ZerosLikeNode

Create an array of zeros with he same shape as the inout array

Input Terminals

array (np.array):

an array to copy the shape of

Output Terminals

zeros (np.array):

an array with the same shape, but filled with zeros

opencv

AverageColorNode

Extract the average color in the shape

Input Terminals

image (np.ndarray):

a color image

circles (list):

list of circles (x, y, r)

Output Terminals

output (np.ndarray):

list of circles (x, y, r)

DrawCircleNode

Draw circles on an image

Input Terminals

image (np.ndarray):

a color image

circles (list):

list of circles (x, y, r)

Output Terminals

output (np.ndarray):

copy of the image with the circles drawn on it.

HoughCircleNode

Detect circles in images using Hough Circle

Input Terminals

image (np.ndarray):

a grayscale image

dp (float):

inverse ratio of the accumulator resolution to the image resolution

minDist (float):

minimum distance between the centers of detected circles

param1 (int):

First method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the higher threshold of the two passed to the Canny() edge detector (the lower one is twice smaller).

param2 (int):

Second method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first.

radius (int):

Minimum circle radius.

radius (int):

Maximum circle radius.

Output Terminals

circles (list):

list of circles (x, y, r)

Rgb2GrayNode

Convert a color image (3D array, RGB) to a grayscale image (2D array)

Input Terminals

image (np.ndarray):

a 3D numpy array

Output Terminals

image (np.ndarray):

a 2D numpy array

pandas

CsvNode

Read a csv file into a Pandas DataFrame

Input Terminals

file (str):

path to a file

sep (str):

seporation character (i.e. ,)

dates (bool):

attempt to convert strings to datetime objects

column (str):

the column to use as the index of the DataFrame

Output Terminals

dataframe (pd.DataFrame):

the resulting Pandas DataFrame

SliceNode

Slice a Pandas DataFrame

Input Terminals

dataframe (pf.DataFrame):

the DataFrame to slice

column (str):

the column name to extract from the DataFrame

Output Terminals

series (pd.DataFrame):

the sliced column of the DataFrame

scikitimage

EdgeCannyNode

Edge filter an image using the Canny algorithm.

Input Terminals

image (np.ndarray):

a two dimensional greyscale input image to detect edges on; can be of any dtype.

sigma (float):

Standard deviation of the Gaussian filter.

threshold (float):

Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype’s max.

threshold (float):

Upper bound for hysteresis thresholding (linking edges). If None, high_threshold is set to 20% of dtype’s max.

Output Terminals

edges (np.ndarray):

a 2D binary edge map.

HoughCircleNode

Perform a circular Hough transform. Returns centers and radii.

Input Terminals

edges (np.ndarray):

two dimensional input image with nonzero values representing edges.

radii (np.ndarray, list):

one dimensional input radii at which to compute the Hough transform. floats are converted to integers.

Output Terminals

result (tuple):

a tuple where the first item is a list of centers and the second item is a list of radii.

Label2RGBNode

Return an RGB image where color-coded labels are painted over the image.

Input Terminals

label (np.ndarray):

two dimensional integer array of labels with the same shape as image.

image (np.ndarray):

Image used as underlay for labels. If the input is an RGB image, it’s converted to grayscale before coloring.

Output Terminals

colored (np.ndarray):

The result of blending a cycling colormap (colors) for each distinct value in label with the image, at a certain alpha value.

Rgb2GrayNode

Convert a color image (3D array, RGB) to a grayscale image (2D array)

Input Terminals

image (np.ndarray):

a 3D numpy array

Output Terminals

image (np.ndarray):

a 2D numpy array

SobelNode

Find the edge magnitude using the Sobel transform.

Input Terminals

image (np.ndarray):

a two dimensional image array to process.

Output Terminals

edges (np.ndarray):

a 2D array of the Sobel edge map.

WatershedNode

Find watershed basins in image flooded from given markers.

Input Terminals

image (np.ndarray):

Data array where the lowest value points are labeled first

markers (int, np.ndarray):

int, or ndarray of int, same shape as image. The desired number of markers, or an array marking the basins with the values to be assigned in the label matrix. Zero means not a marker.

Output Terminals

segmentation (np.ndarray):

A labeled matrix of the same type and shape as markers

scipy

BinaryFillHolesNode

Fill holes of a binary image.

Input Terminals

image (np.ndarray):

the array to apply the filter to

Output Terminals

image (np.ndarray):

the resulting array

GaussianFilterNode

Apply a gaussian filter to an input array (image)

Input Terminals

image (np.ndarray):

the array to apply the filter to

sigma (float):

standard deviation for Gaussian kernel

Output Terminals

image (np.ndarray):

the resulting array

ImageReadNode

Read an image file

Input Terminals

file (str):

path to the image file

Output Terminals

image (np.ndarray):

the resulting array

LabelNode

Label an image

Input Terminals

image (np.ndarray):

the array to apply the filter to

Output Terminals

image (np.ndarray):

the resulting array

MedianFilterNode

Apply a gaussian filter to an input array (image)

Input Terminals

image (np.ndarray):

the array to apply the filter to

size (float):

gives the shape that is taken from the input array, at every element position, to define the input to the filter function

Output Terminals

image (np.ndarray):

the resulting array

ReadWave

Given a wave file, return the sample rate and data

Input Terminals

file (str):

The file to read

Output Terminals

rate (int):

The sample rate (Hz).

data (np.ndarray):

the data

seaborn

BasePlotNode

base node for all the seaborn nodes, not meant to be used directly

CorrelationMatrixNode

Plot a correlation matrix of a Pandas DataFrame. The correltation matrix is calculated with Pandas DataFrame.corr() method. The Plot is generated using Seaborn’s heatmap method.

Input Terminals

data (pd.DataFrame):

A Pandas DataFrame.

DistributionNode

Flexibly plot a univariate distribution of observations.

This function combines the matplotlib hist function (with automatic calculation of a good default bin size) with the seaborn kdeplot() and rugplot() functions. It can also fit scipy.stats distributions and plot the estimated PDF over the data.

Input Terminals

data (pd.Series, 1d-array, or list):

Observed data. If this is a Series object with a name attribute, the name will be used to label the data axis.

hist (bool):

Whether to plot a (normed) histogram.

kde (bool):

Whether to plot a gaussian kernel density estimate.

rug (bool):

Whether to draw a rugplot on the support axis.

HeatMapNode

Plot rectangular data as a color-encoded matrix.

This function tries to infer a good colormap to use from the data, but this is not guaranteed to work, so take care to make sure the kind of colormap (sequential or diverging) and its limits are appropriate.

Input Terminals

data (np.ndarray or pd.DataFrame):

2D dataset that can be coerced into an ndarray. If a Pandas DataFrame is provided, the index/column information will be used to label the columns and rows.

KdeNode

Fit and plot a univariate or bivariate kernel density estimate.

Input Terminals

data (np.ndarray):

1d array-like input data.

data2 (np.ndarray):

Second 1d array-like input data. If present, a bivariate KDE will be estimated.

LineplotNode

Plot one or more timeseries with flexible representation of uncertainty.

This function is intended to be used with data where observations are nested within sampling units that were measured at multiple timepoints.

It can take data specified either as a long-form (tidy) DataFrame or as an ndarray with dimensions (unit, time) The interpretation of some of the other parameters changes depending on the type of object passed as data.

Input Terminals

data (np.ndarray or pd.DataFrame):

Tidy (“long-form”) dataframe where each column is a variable and each row is an observation.

y (string): names of variables in data or vector data, optional

RegNode

Plot data and a linear regression model fit.

There are a number of mutually exclusive options for estimating the regression model: order, logistic, lowess, robust, and logx. See the parameter docs for more information on these options.

Input Terminals

data (pd.DataFrame):

Tidy (“long-form”) dataframe where each column is a variable and each row is an observation.

y (string, series, or vector array):

Input variables. If strings, these should correspond with column names in data. When pandas objects are used, axes will be labeled with the series name.

order (int):

If order is greater than 1, use numpy.polyfit to estimate a polynomial regression.

RugNode

Plot datapoints in an array as sticks on an axis.

Input Data

data (np.ndarray):

1D array of observations.

key (str):

name of the column in the data frame to plot.

shell

ShellRun

Runs a shell command

Input Terminals

Command (str):

The command to execute

Through Terminals

directory (str):

The working directory to run the command

structure

BaseStructure

This is the base of the structures and is not meant to be used directly

CaseNode

Node for creating multiple cases

Input Terminals

<dynamic> (any):

user created input terminal(s)

case (int):

used to select the active case

Output Terminals

<dynamic> (any):

user created output terminal(s)

FunctionNode

Node for creating custom functions

Input Terminals

<dynamic> (any):

user created input terminal(s)

Output Terminals

functionOut (python function):

returns the function which consists of the custom code

returnOut (any):

the value of the output(s) connected

GroupNode

Node for creating groups of nodes

Input Terminals

<dynamic> (any):

user created input terminal(s)

Output Terminals

<dynamic> (any):

user created output terminal(s)

Loop

Node for creating for and while loops

Input Terminals

<dynamic> (any):

user created input terminal(s) - terminals can be passthough, indexed, and shift

Output Terminals

<dynamic> (any):

user created output terminal(s)

ParLoop

Node for creating for and while loops

Input Terminals

<dynamic> (any):

user created input terminal(s) - terminals can be passthough, indexed, and shift

Output Terminals

<dynamic> (any):

user created output terminal(s)

sympy

SimpleCalc

Dynamically creates a symbolic equation based on variable inputs and calculates the result as a float

Input Terminals

variables (str):

a comma separated string of input variables allowed in the equation

equation (str):

a string that represents a symbolic equation that is a combination of the variables

Output Terminals

result (float):

a float that is the evaluated result of the equation

SymbolicFunction

convert equation to a function

Input Terminals

equation (str):

an equation to be converted to a python object

symbols (str):

a list of symbols

Output Terminals

function (str):

the resulting function

SympyAdd

Adds multiple sympy objects together

Input Terminals

inputs (sympy):

multiple sympy objects

Output Terminals

string (str):

a single output string representation of the equation in the sympy terminal

sympy (sympy):

a single output sympy object which is the sum of all of the input sympy objects

SympyDivide

Divides one sympy object into another

Input Terminals

dividend (sympy):

a single output sympy object (numerator)

divisor (sympy):

a single output sympy object (denominator)

Output Terminals

string (str):

a single output string representation of the equation in the sympy terminal

sympy (sympy):

a single output sympy object which is the quotient of the dividend and divisor

SympyEquation

Creates a symbolic equation object based on the input to the equation terminal

Input Terminals

equation (str):

a single input string that will be parsed into an sympy equation object

Output Terminals

sympy (sympy):

a single output sympy object that represents the equation

SympyMultiply

Multiplies multiple sympy objects together

Input Terminals

inputs (sympy):

multiple sympy objects

Output Terminals

string (str):

a single output string representation of the equation in the sympy terminal

sympy (sympy):

a single output sympy object which is the product of all of the input sympy objects

SympySimplify

Simplifies a sympy expression

Input Terminals

input (sympy):

a single input sympy object

Output Terminals

string (str):

a single output string representation of the equation in the sympy terminal

sympy (sympy):

a single output sympy object which is the sympy simplify result

SympySubtract

Subtracts one sympy object into another

Input Terminals

minuend (sympy):

a single input sympy object

subtrahend (sympy):

a single input sympy object

Output Terminals

string (str):

a single output string representation of the equation in the sympy terminal

sympy (sympy):

a single output sympy object which is the difference of the minuend and subtrahend

SympyToString

Creates a string object from a sympy object

Input Terminals

equation (sympy):

a single input sympy object

Output Terminals

string (str):

a single output string representation of the equation input

types

BoolNode

Bool Type

Output Terminals

(int):

the bool value of the checkbox

FloatNode

Float Type

Output Terminals

(float):

the float value of the spinbox

IntNode

Integer Type

Output Terminals

(int):

the integer value of the spinbox

ListNode

List Type

Input Terminals

<dynamic>(any)

the values to put in the list

Output Terminals

list(list):

the list of input values

RangeNode

Range Type

Input Terminals

start(int):

the start value of the range

stop(int):

the ending value of the range

step(int):

the number of steps between start and stop

Output Terminals

range(list):

the list of int values created

SetNode

Set Type - creates a set from the provided input

Input Terminals

in(any):

the values to add to the set

Output Terminals

out(any):

a set of the input values

StringNode

String Type

Output Terminals

(string):

the string value of the line edit

wavelets

BasePlotNode

base node for all the seaborn nodes, not meant to be used directly

EnergyPlot

Given a wavlet, plot it.

Input Terminals

wavlet (str):

The wavelet to plot

Output Termianls

object (pywt.Wavelet):

The resulting wavlet object.

FreqBands

Given a sample frequency, determine the frequency bands

Input Terminals

frequency (float):

The sample frequency in Hz.

frequency (float):

The sample frequency in Hz.

MultilevelDiscreteWaveletTransform

Perform a multilevel Discrete Wavelet Transform (DWT).

Input Terminals

signal (iterable):

The signal to compute the DWT on

wavelet (str):

The wavelet to plot

levels (int):

The number of levels to perform the DWT. Note: if levels is greater than the maximum levels, will default to maximum levels.

mode (str):

The method to use to treat the ends of the signal.

Output Termianls

coefficients (list):

The resulting coefficients.

MultilevelInverseWaveletTransform

Perform a multilevel Inverse Discrete Wavelet Transform (DWT) for each set of coefficients provided.

Input Terminals

coefficients (iterable):

A list of coefficients: An, Dn, Dn-1 … D0

wavelet (str):

The wavelet to plot

mode (str):

The method to use to treat the ends of the signal.

Output Termianls

levels (list):

A list of the resulting reconstructed signals for each set of coefficients

Wavelet

Given a wavlet, plot it.

Input Terminals

wavlet (str):

The wavelet to plot

Output Termianls

object (pywt.Wavelet):

The resulting wavlet object.