Examples

import deepvisual as dv
import pandas as pd
 
# Sample social network data
df = pd.DataFrame({
    'from': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob'],
    'to': ['Bob', 'Charlie', 'Alice', 'Charlie', 'Alice'],
    'relation': ['friends', 'colleagues', 'family', 'colleagues', 'friends']
})
 
# Create a triplet graph visualization
dv.visualize_triplet_graph(
    df,
    edge_color="blue",
    node_color="lightblue",
    node_text_color="black",
    background_color="white"
)

import deepvisual as dv
import pandas as pd
 
# Sample project dependencies
df = pd.DataFrame({
    'from': ['Frontend', 'Backend', 'Database', 'API'],
    'to': ['Backend', 'Database', 'API', 'Frontend']
})
 
# Create a doublet link visualization
dv.visualize_link_doublet(
    df,
    edge_color="green",
    node_text_color="black",
    background_color="white"
)

import deepvisual as dv
import pandas as pd
import deepcore as dc
 
# Sample workflow data
df = pd.DataFrame({
    'from': ['Start', 'Process', 'Review', 'Process', 'End'],
    'to': ['Process', 'Review', 'Process', 'End', 'Start']
})
 
# Let's sort using the deepcore library
dc.sort_duoblet(df)
 
# Create a doublet link visualization
dv.visualize_link_doublet(
    df,
    loop_color='red',
    edge_color='black',
    inter_edge_color='blue',
    title='Workflow Process'
)

import deepvisual as dv
import pandas as pd
 
# Sample data
df = pd.DataFrame({
    'from': ['A', 'B', 'C', 'D'],
    'to': ['B', 'C', 'D', 'A']
})
 
# Create a customized visualization
dv.visualize_doblet_graph(
    df,
    edge_color="purple",
    node_text_color="white",
    background_color="darkgray",
    figsize=(12, 10),
    curvature=0.3,
    arrow_style="->,head_length=0.8,head_width=0.6",
    connection_style="arc3,rad=0.2"
)

import deepvisual as dv
import pandas as pd
import matplotlib.pyplot as plt
 
# Sample data
df = pd.DataFrame({
    'from': ['Input', 'Process', 'Output'],
    'to': ['Process', 'Output', 'Input']
})
 
# Create the visualization
fig = dv.visualize_link_doublet(
    df,
    loop_color='orange',
    edge_color='blue',
    inter_edge_color='green',
    title='Interactive Process'
)
 
# Add interactivity
plt.ion()  # Turn on interactive mode
plt.show()
 
# The plot will remain interactive in Jupyter