parallelplot(data::DataFrame, _Arguments_)

Arguments

Examples

julia> using DataFrames

julia> fig, ax, sc = parallelplot(DataFrame(height=160:180,weight=60:80,age=20:40))
FigureAxisPlot()

julia> display(fig)
CairoMakie.Screen{IMAGE}

Using DrWatson with ParallelPlot

julia> using DataFrames, DrWatson, ParallelPlots

julia> function exec_simulation(d::Dict, results)
           @unpack launch_angles, initial_velocities = d
           max_height = initial_velocities * launch_angles
           push!(results, [
               initial_velocities,
               launch_angles,
               max_height,
           ])
           return results
       end;

julia> initial_velocities = [40.0, 50.0];

julia> launch_angles = [30.0, 60.0];

julia> allparams = Dict(
           "initial_velocities" => initial_velocities,
           "launch_angles" => launch_angles,
       );

julia> dicts = dict_list(allparams);

julia> results = DataFrame(
           initial_velocity=Float64[],
           launch_angle=Float64[],
           max_height=Float64[],
       );

julia> for d in dicts
           results = exec_simulation(d, results)
       end;

julia> fig = parallelplot(results, curve=true, figure = (size = (1000, 600),));

julia> display(fig);

# If you want to set the size of the plot
julia> parallelplot( DataFrame(height=160:180,weight=60:80,age=20:40), figure = (resolution = (300, 300),) )

# You can update the Graph with Observables as well 
julia> df_observable = Observable(DataFrame(height=160:180,weight=60:80,age=20:40))
julia> fig, ax, sc = parallelplot(df_observable)

# If you want to add a Title for the Figure, sure you can!
julia> parallelplot(DataFrame(height=160:180,weight=reverse(60:80),age=20:40),title="My Title")

# If you want to specify the axis labels, make sure to use the same number of labels as you have axis!
julia> parallelplot(DataFrame(height=160:180,weight=reverse(60:80),age=20:40), feature_labels=["Height","Weight","Age"])

# Adjust Color and and Feature
parallelplot(df,
		# You choose which axis/feature should be in charge for the coloring
        color_feature="weight",
        # you can as well select, which Axis should be shown
        feature_selection=["height","age","income"],
        # and label them as you like
        feature_labels=["Height","Age","Income"],
        # you can change the ColorMap (https://docs.makie.org/dev/explanations/colors)
        colormap=:thermal,
        # ...and can choose to display the color legend.
        # If this Attribute is not set,
        # it will only show the ColorBar, when the color feature is not in the selected feature
        show_color_legend = true
    )

# Adjust the Axis scale
parallelplot(df,
        feature_selection=["height","age","income"],
        scale=[log2, identity, log10]
    )

axis_title!(
	scene,
	endpoints::Observable,
	title::String;
	titlegap = Observable(4.0f0),
)

This Function will create the Axis Label for a Axis

calc_factor(min::T, max::T, x::T, fun::Function):: Float64 where {T<:Real}

In Linear Axis represenatation, values between 0-1 are linear distributed. Due to the Logarithmfunction, we need to distribute the values with the given log values to match the axis

Input:

• min::T,
• max::T,
• x::T,
• fun::Function –> [identity, log, log2, log10, sqrt]

Output:

• the distribution with the given function beween 0 and 1

calc_y_coordinate(parsed_data, limits, height, offset, feature_index :: Number, sample_index :: Number, scale_list) :: Number

This function will return the y position in the scene, depending of the scale if set

Output:

• the y position of the datapoint in the scene

calculate_color(pp::ParallelPlot, data::DataFrame) :: Tuple{AbstractString, Vector{Real}, Real, Real}

Calculates the Color values for the Lines

Input:

• pp::ParallelPlot
• data::DataFrame

Output:

• colorcol result of `getcolor_col`
• colorvalues The List of Values of the `colorcol`. Needed to calculate the Color for each Line
• colormin The min value of `colorvalues`. To Calculate the ColorRange
• colormax The max value of `colorvalues`. To Calculate the ColorRange

create_scale_list(numberFeatures :: Number, scale_list)

check the length of the given scale Attribute. Throws an Error if the Length does not match the amount of axis/features If scale is not set, identity, so linear will be used for all axis. if the length of the scale attribute does not fit, an assert error will be thrown

Input:

• numberFeatures::Number
• scale_list nothing or Vector e.g. [log2, log10, identity]

Output:

• given scale_list or vector of [identity, identity, ...] with the length of axis/features

Throws

Assertion, if amount of scales in the scale list does not match the amount of axis/features

draw_axis(
	scene,
	width::Number,
	height::Number,
	offset::Number,
	limits,
	labels,
	numberFeatures::Number,
	scale_list
)

Draws the Axis/Feature vertical Axis Lines on the given Scene

draw_lines(
	scene,
	pp,
	data,
	width::Number,
	height::Number,
	offset::Number,
	limits,
	numberFeatures::Number,
	scale_list,
	sampleSize::Number,
	parsed_data,
	color_values,
	color_min,
	color_max
)

Function to Draw the Lines connecting the Values on each Axis into the Plot

get_color_col(pp::ParallelPlot, data::DataFrame) :: AbstractString

get the name of the Column, on which the Color should be dependent

Input:

• pp::ParallelPlot
• data::DataFrame

Output:

• AbstractString

input_data_check(data::DataFrame)

checks the Input Data if the size is correct and no missing values are available

Input:

• DataFrame

Output:

• none

Trows

Throws error on wrong DF

interpolate(last_x::Float64, current_x::Float64, last_y::Float64, current_y::Float64, x::Float64)::Float64

Interpolates the Y Value between the given current/last(x/y) point with the given x value.

Input:

• old and New Coordinate (x/y Value)
• current x Value

Output:

• current, interpolated y Value

show_color_legend!(pp) :: Bool

Returns Boolean if the Color Legend/Bar on the right should be shown

Input:

• pp::ParallelPlot

Output:

• boolean if show_color_legend from the Arguments is set, return this value. Else show, when colorfeature is not in featureselection