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’.
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 exsiting 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 seporate 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
matplotlib¶
BasePlotNode¶
base node for all the matplotlib nodes, not meant to be used directly
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.)
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 witht 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
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)
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
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
seaborn¶
BasePlotNode¶
base node for all the seaborn nodes, not ment 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.
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.
TsNode¶
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):
- Data for the plot. Should either be a “long form” dataframe or an array with dimensions (unit, time, condition). In both cases, the condition field/dimension is optional. The type of this argument determines the interpretation of the next few parameters. When using a DataFrame, the index has to be sequential.
- value (string):
- Either the name of the field corresponding to the data values in the data DataFrame (i.e. the y coordinate) or a string that forms the y axis label when data is an array.
- time (string or series-like):
- Either the name of the field corresponding to time in the data DataFrame or x values for a plot when data is an array. If a Series, the name will be used to label the x axis.
- unit (string):
- Field in the data DataFrame identifying the sampling unit (e.g. subject, neuron, etc.). The error representation will collapse over units at each time/condition observation. This has no role when data is an array.
- err_style (string or list of strings or None):
- Names of ways to plot uncertainty across units from set of {ci_band, ci_bars, boot_traces, boot_kde, unit_traces, unit_points}. Can use one or more than one method.
shell¶
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)
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 simplifiy 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
types¶
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
wavelets¶
BasePlotNode¶
base node for all the seaborn nodes, not ment 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