mXrap - Xplot

Xplot

This library provides functions to perform plotting and generation of various 2D charts

Status	Name
	Generate.ids ( `Table` ↑Table, `Function` ↑write_ID ) Writes 1 to size of table to the selected column. Useful for generating IDs for an output table.
	Generate.linear ( `Table` ↑Table, `Function` ↑write_Field, `Number` min_value, `Number` max_value, `Number` num_steps ) Outputs a linear range of numbers based on min value, max value and desired result size, to an output table column
	Generate.linear_array ( `Array` ↑array, `Number` min_value, `Number` max_value, `Number` num_steps ) Outputs a linear range of numbers based on min value, max value and desired result size, to an output array
	`Number` Generate.linearInterval ( `Table` ↑Table, `Function` ↑write_Field, `Number` min_value, `Number` max_value, `Number` interval ) Outputs a linear range of numbers based on min value, max value and desired interval, to an output table column
	`Number` Generate.linearInterval_array ( `Array` ↑array, `Number` min_value, `Number` max_value, `Number` interval ) Outputs a linear range of numbers based on min value, max value and desired interval, to an output array
	Generate.log ( `Table` ↑Table, `Function` ↑write_Field, `Number` min_value, `Number` max_value, `Number` num_steps ) Outputs a logarithmic range of numbers based on min value, max value and desired result size, to an output table column
	Generate.log_array ( `Array` ↑array, `Number` min_value, `Number` max_value, `Number` num_steps ) Outputs a logarithmic range of numbers based on min value, max value and desired result size, to an output array
	`Number` Generate.logInterval ( `Table` ↑Table, `Function` ↑write_Field, `Number` min_value, `Number` max_value, `Number` interval ) Outputs a logarithmic range of numbers based on min value, max value and desired interval, to an output table column
	`Number` Generate.logInterval_array ( `Array` ↑array, `Number` min_value, `Number` max_value, `Number` interval ) Outputs a logarithmic range of numbers based on min value, max value and desired interval, to an output array
	Generate.values ( `Table` ↑Table, `Function` ↑write_FieldY, `Object` ↓f, `Table` ↓Table, `Function` ↓read_FieldX ) Computes the output of a function f(x) based on a Table's input field (read_FieldX), to an output field (write_FieldY).
	Generate.values_array ( `Array` ↑y_array, `Object` ↓f, `Number` ↓x_size, `Array` ↓x_array ) Computes the output of a function f(x) based on an input array range (x_array) of size (x_size), to an output array (y_array)
	`Function` Scale.createInvLogScaler ( `Number` ↓minX, `Number` ↓maxX, `Number` ↓minY, `Number` ↓maxY ) Creates a function for inverse log scaling values from minX..maxX to minY..maxY. It uses the part of the log curve from 2..98.
	`Function` Scale.createLinearScaler ( `Number` ↓minX, `Number` ↓maxX, `Number` ↓minY, `Number` ↓maxY ) Creates a function for linearly scaling values from minX..maxX to minY..maxY
	`Function` Scale.createLogScaler ( `Number` ↓minX, `Number` ↓maxX, `Number` ↓minY, `Number` ↓maxY ) Creates a function for log scaling values from minX..maxX to minY..maxY. It uses the part of the log curve from 2..98.
	Transform.convolve ( `Table` ⇅Table, `Function` ↓read_Value, `Function` ↑write_Value, `Vector` ↓convolve_vec, `Number` ↓convolve_vec_size ) Processes values in this table by convolution. At the start and end of the table, where there are insufficient values to compute the convolved value, the output value is left blank.
	`Number` Transform.convolve_array ( `Array` ↑values, `Vector` ↓convolve_vec, `Number` ↓convolve_vec_size, `Array` ↓values, `Number` ↓values_length ) Processes values in an input array by convolution and returns it in another array. At the start and end of the array, where there are insufficient values to compute the convolved value, output for those values are omitted.
	Transform.convolveMirror ( `Table` ⇅Table, `Function` ↓read_Value, `Function` ↑write_Value, `Vector` ↓convolve_vec, `Number` ↓convolve_vec_size ) Processes values in this table by convolution. At the start and end of the table, where there are insufficient values to compute the convolved value, input data is mirrored to fill in the missing input values.
	`Number` Transform.convolveMirror_array ( `Array` ↑values, `Vector` ↓convolve_vec, `Number` ↓convolve_vec_size, `Array` ↓values, `Number` ↓values_length ) Processes values in an input array by convolution and returns it in another array. At the start and end of the array, where there are insufficient values to compute the convolved value, input data is mirrored to fill in the missing input values.

Category: Generate

Functions for generation of values

Lib.Generate.ids ( ↑Table, ↑write_ID ) Beta

Writes 1 to size of table to the selected column. Useful for generating IDs for an output table.

Parameters:

Table ↑Table
Function ↑write_ID

Lib.Generate.linear ( ↑Table, ↑write_Field, min_value, max_value, num_steps ) Beta

Outputs a linear range of numbers based on min value, max value and desired result size, to an output table column

Parameters:

Table ↑Table
Function ↑write_Field
Number min_value
Number max_value
Number num_steps

Lib.Generate.linear_array ( ↑array, min_value, max_value, num_steps ) Beta

Outputs a linear range of numbers based on min value, max value and desired result size, to an output array

Parameters:

Array ↑array
Number min_value
Number max_value
Number num_steps

numOutput = Lib.Generate.linearInterval ( ↑Table, ↑write_Field, min_value, max_value, interval ) Beta

Outputs a linear range of numbers based on min value, max value and desired interval, to an output table column

Parameters:

Table ↑Table
Function ↑write_Field
Number min_value
Number max_value
Number interval

Returns: Number numOutput - number of numbers generated

numOutput = Lib.Generate.linearInterval_array ( ↑array, min_value, max_value, interval ) Beta

Outputs a linear range of numbers based on min value, max value and desired interval, to an output array

Parameters:

Array ↑array
Number min_value
Number max_value
Number interval

Returns: Number numOutput - number of numbers generated

Lib.Generate.log ( ↑Table, ↑write_Field, min_value, max_value, num_steps ) Beta

Outputs a logarithmic range of numbers based on min value, max value and desired result size, to an output table column

Parameters:

Table ↑Table
Function ↑write_Field
Number min_value
Number max_value
Number num_steps

Lib.Generate.log_array ( ↑array, min_value, max_value, num_steps ) Beta

Outputs a logarithmic range of numbers based on min value, max value and desired result size, to an output array

Parameters:

Array ↑array
Number min_value
Number max_value
Number num_steps

numOutput = Lib.Generate.logInterval ( ↑Table, ↑write_Field, min_value, max_value, interval ) Beta

Outputs a logarithmic range of numbers based on min value, max value and desired interval, to an output table column

Parameters:

Table ↑Table
Function ↑write_Field
Number min_value
Number max_value
Number interval

Returns: Number numOutput - number of numbers generated

numOutput = Lib.Generate.logInterval_array ( ↑array, min_value, max_value, interval ) Beta

Outputs a logarithmic range of numbers based on min value, max value and desired interval, to an output array

Parameters:

Array ↑array
Number min_value
Number max_value
Number interval

Returns: Number numOutput - number of numbers generated

Lib.Generate.values ( ↑Table, ↑write_FieldY, ↓f, ↓Table, ↓read_FieldX ) Beta

Computes the output of a function f(x) based on a Table's input field (read_FieldX), to an output field (write_FieldY).

Parameters:

Table ↑Table
Function ↑write_FieldY
Object ↓f - may be a normal JS function that takes in a single input number, or a data object from Xfit library
Table ↓Table
Function ↓read_FieldX

Lib.Generate.values_array ( ↑y_array, ↓f, ↓x_size, ↓x_array ) Beta

Computes the output of a function f(x) based on an input array range (x_array) of size (x_size), to an output array (y_array)

Parameters:

Array ↑y_array - output array
Object ↓f - may be a normal JS function that takes in a single input number, or a data object from Xfit library
Number ↓x_size - the size of the input array
Array ↓x_array - input array

Category: Scale

Functions for scaling values

↑func = Lib.Scale.createInvLogScaler ( ↓minX, ↓maxX, ↓minY, ↓maxY ) Beta

Creates a function for inverse log scaling values from minX..maxX to minY..maxY. It uses the part of the log curve from 2..98.

Parameters:

Number ↓minX - - minimum x. Any input x below this value is set to this value.
Number ↓maxX - - maximum x. Any input x above this value is set to this value. maxX must always >= minX
Number ↓minY - - minimum y. The scaled value returned corresponding to minX.
Number ↓maxY - - maximum y. The scaled value returned corresponding to maxX. Note minY may be larger than maxY, in the case of an inverse scaling.

Returns: Function ↑func - - y = f(x), where y is the inverse log scaled value of x.

↑func = Lib.Scale.createLinearScaler ( ↓minX, ↓maxX, ↓minY, ↓maxY ) Beta

Creates a function for linearly scaling values from minX..maxX to minY..maxY

Parameters:

Number ↓minX - - minimum x. Any input x below this value is set to this value.
Number ↓maxX - - maximum x. Any input x above this value is set to this value. maxX must always >= minX
Number ↓minY - - minimum y. The scaled value returned corresponding to minX.
Number ↓maxY - - maximum y. The scaled value returned corresponding to maxX. Note minY may be larger than maxY, in the case of an inverse scaling.

Returns: Function ↑func - - y = f(x), where y is the linearly scaled value of x.

↑func = Lib.Scale.createLogScaler ( ↓minX, ↓maxX, ↓minY, ↓maxY ) Beta

Creates a function for log scaling values from minX..maxX to minY..maxY. It uses the part of the log curve from 2..98.

Parameters:

Number ↓minX - - minimum x. Any input x below this value is set to this value.
Number ↓maxX - - maximum x. Any input x above this value is set to this value. maxX must always >= minX
Number ↓minY - - minimum y. The scaled value returned corresponding to minX.
Number ↓maxY - - maximum y. The scaled value returned corresponding to maxX. Note minY may be larger than maxY, in the case of an inverse scaling.

Returns: Function ↑func - - y = f(x), where y is the log scaled value of x.

Category: Transform

Functions for transforming values

Lib.Transform.convolve ( ⇅Table, ↓read_Value, ↑write_Value, ↓convolve_vec, ↓convolve_vec_size ) Beta

Processes values in this table by convolution.

At the start and end of the table, where there are insufficient values to compute the convolved value, the output value is left blank.

Parameters:

Table ⇅Table - - Table containing the input values and has the field to write the convolved value to.
Function ↓read_Value - - read function for the input values.
Function ↑write_Value - - write function for the convolved values.
Vector ↓convolve_vec - - coefficients of the convolution vector.
Number ↓convolve_vec_size - - size of the convolution vector specified above. This must be a positive odd integer.

↑values_length = Lib.Transform.convolve_array ( ↑values, ↓convolve_vec, ↓convolve_vec_size, ↓values, ↓values_length ) Beta

Processes values in an input array by convolution and returns it in another array.

At the start and end of the array, where there are insufficient values to compute the convolved value, output for those values are omitted.

Parameters:

Array ↑values - - output array.
Vector ↓convolve_vec - - coefficients of the convolution vector.
Number ↓convolve_vec_size - - size of the convolution vector specified above.
Array ↓values - - input values array.
Number ↓values_length - - size of input values array.

Returns: Number ↑values_length - - number of elements in output array.

Lib.Transform.convolveMirror ( ⇅Table, ↓read_Value, ↑write_Value, ↓convolve_vec, ↓convolve_vec_size ) Beta

Processes values in this table by convolution.

At the start and end of the table, where there are insufficient values to compute the convolved value, input data is mirrored to fill in the missing input values.

Parameters:

Table ⇅Table - - Table containing the input values and has the field to write the convolved value to.
Function ↓read_Value - - read function for the input values.
Function ↑write_Value - - write function for the convolved values.
Vector ↓convolve_vec - - coefficients of the convolution vector.
Number ↓convolve_vec_size - - size of the convolution vector specified above. This must be a positive odd integer.

↑values_length = Lib.Transform.convolveMirror_array ( ↑values, ↓convolve_vec, ↓convolve_vec_size, ↓values, ↓values_length ) Beta

Parameters:

Array ↑values - - output array.
Vector ↓convolve_vec - - coefficients of the convolution vector.
Number ↓convolve_vec_size - - size of the convolution vector specified above. This must be a positive odd integer.
Array ↓values - - input values array.
Number ↓values_length - - size of input values array.

Returns: Number ↑values_length - - number of elements in output array.

Libraries

Categories

Classes

Xplot

Category: Generate

Category: Scale

Category: Transform