Scatter Plot With A Legend Using Python View Node For KNIME 4.7

[HeatherPikairos]

Hi Community!

I have been working on another Matplotlib visualisation using the -Python View- node. This time a scatter plot that uses different colours for different clusters. The plot is also accompanied by a legend, whose position can be changed based on user selection in the interactive view of the component.

The workflow can be found on the hub here:

20230502 Scatter Plot with a Legend using Python View for KNIME 4.7 –...

Again, i hope this can be useful to someone

Best wishes
Heather

[roberto_cadili]

That’s great, Heather! Thanks for sharing!

[HeatherPikairos]

Thank you! No problem

[HeatherPikairos]

Sharing the code for this to improve searchability

import knime.scripting.io as knio

from io import BytesIO
import numpy as np
import matplotlib.pyplot as plt

# Only use numeric columns
#data = knio.input_tables[0]._get_numeric_data()
data = knio.input_tables[0].to_pandas()
#matrix = data.select_dtypes(include='number').to_numpy()

print(data)

# Figure with plots construction
fig, ax = plt.subplots()

# Matrix construction
#matrix = input_table.values
matrix = data.values
# Color column selection
#colors = data[knio.flow_variables['color_column_selection']]
colors = data['Color']
# Set of unique colors
colors_set = np.unique( colors)


print(matrix)
print(colors)
print(colors_set)


#for every color in the set, extract those who matches 
#the condition in a submatrix
for current_color in colors_set:
    #condition
    #condition = colors == current_color
    condition = matrix[ :, 0] == current_color
    # print( condition)
    #submatrix creation with only two columns of numeric numbers
    color_submatrix = matrix[ np.nonzero( condition), 1:3]
    #squeeze to remove a dimension
    color_submatrix = color_submatrix.squeeze()

    # Abscise column of scatter plot
    x = color_submatrix[ :, 0]

    # Ordinate column of scatter plot
    y = color_submatrix[ :, 1]

    # Color selection for points in scatter plot
    color_tab = "tab:" + str(current_color)

    print(color_tab)

    # Scatter plot construction
    scatter = ax.scatter( x, y, c = color_tab, label = current_color,
    alpha =knio.flow_variables['color_transparency'])


# Legend option
if knio.flow_variables['include_legend']:
    legend = ax.legend(loc = knio.flow_variables['legend_location'], 
    title=knio.flow_variables['legend_title'])
    ax.add_artist(legend)

# Grid option
if knio.flow_variables['include_grid']:
    ax.grid(True)

#plot title set
ax.set_title(knio.flow_variables['plot_title'])
ax.set_xlabel(data.columns[1])
ax.set_ylabel(data.columns[2])

# Replace row ID by number
#data.index = range(0, len(data))
# Create buffer to write into
buffer = BytesIO()
# Create plot and write it into the buffer
fig.savefig(buffer, format='svg')
# The output is the content of the buffer
output_image = buffer.getvalue()


# Assign the figure to the output_view variable
knio.output_view = knio.view(fig)  # alternative: knio.view_matplotlib()