How to use "edge bundling" with networkx and matplotlib in Python?

Question

I've created a toy graph with the iris dataset. My layout is from PCA ordination which separates out the nodes nicely.

I've recently discovered edge bundling. Does anybody know of a way to do this with matplotlib and networkx?

from sklearn.decomposition import PCA
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np


# Data
X_iris = pd.DataFrame({'sepal_length': {'iris_0': 5.1, 'iris_1': 4.9, 'iris_2': 4.7, 'iris_3': 4.6, 'iris_4': 5.0, 'iris_5': 5.4, 'iris_6': 4.6, 'iris_7': 5.0, 'iris_8': 4.4, 'iris_9': 4.9, 'iris_10': 5.4, 'iris_11': 4.8, 'iris_12': 4.8, 'iris_13': 4.3, 'iris_14': 5.8, 'iris_15': 5.7, 'iris_16': 5.4, 'iris_17': 5.1, 'iris_18': 5.7, 'iris_19': 5.1, 'iris_20': 5.4, 'iris_21': 5.1, 'iris_22': 4.6, 'iris_23': 5.1, 'iris_24': 4.8, 'iris_25': 5.0, 'iris_26': 5.0, 'iris_27': 5.2, 'iris_28': 5.2, 'iris_29': 4.7, 'iris_30': 4.8, 'iris_31': 5.4, 'iris_32': 5.2, 'iris_33': 5.5, 'iris_34': 4.9, 'iris_35': 5.0, 'iris_36': 5.5, 'iris_37': 4.9, 'iris_38': 4.4, 'iris_39': 5.1, 'iris_40': 5.0, 'iris_41': 4.5, 'iris_42': 4.4, 'iris_43': 5.0, 'iris_44': 5.1, 'iris_45': 4.8, 'iris_46': 5.1, 'iris_47': 4.6, 'iris_48': 5.3, 'iris_49': 5.0, 'iris_50': 7.0, 'iris_51': 6.4, 'iris_52': 6.9, 'iris_53': 5.5, 'iris_54': 6.5, 'iris_55': 5.7, 'iris_56': 6.3, 'iris_57': 4.9, 'iris_58': 6.6, 'iris_59': 5.2, 'iris_60': 5.0, 'iris_61': 5.9, 'iris_62': 6.0, 'iris_63': 6.1, 'iris_64': 5.6, 'iris_65': 6.7, 'iris_66': 5.6, 'iris_67': 5.8, 'iris_68': 6.2, 'iris_69': 5.6, 'iris_70': 5.9, 'iris_71': 6.1, 'iris_72': 6.3, 'iris_73': 6.1, 'iris_74': 6.4, 'iris_75': 6.6, 'iris_76': 6.8, 'iris_77': 6.7, 'iris_78': 6.0, 'iris_79': 5.7, 'iris_80': 5.5, 'iris_81': 5.5, 'iris_82': 5.8, 'iris_83': 6.0, 'iris_84': 5.4, 'iris_85': 6.0, 'iris_86': 6.7, 'iris_87': 6.3, 'iris_88': 5.6, 'iris_89': 5.5, 'iris_90': 5.5, 'iris_91': 6.1, 'iris_92': 5.8, 'iris_93': 5.0, 'iris_94': 5.6, 'iris_95': 5.7, 'iris_96': 5.7, 'iris_97': 6.2, 'iris_98': 5.1, 'iris_99': 5.7, 'iris_100': 6.3, 'iris_101': 5.8, 'iris_102': 7.1, 'iris_103': 6.3, 'iris_104': 6.5, 'iris_105': 7.6, 'iris_106': 4.9, 'iris_107': 7.3, 'iris_108': 6.7, 'iris_109': 7.2, 'iris_110': 6.5, 'iris_111': 6.4, 'iris_112': 6.8, 'iris_113': 5.7, 'iris_114': 5.8, 'iris_115': 6.4, 'iris_116': 6.5, 'iris_117': 7.7, 'iris_118': 7.7, 'iris_119': 6.0, 'iris_120': 6.9, 'iris_121': 5.6, 'iris_122': 7.7, 'iris_123': 6.3, 'iris_124': 6.7, 'iris_125': 7.2, 'iris_126': 6.2, 'iris_127': 6.1, 'iris_128': 6.4, 'iris_129': 7.2, 'iris_130': 7.4, 'iris_131': 7.9, 'iris_132': 6.4, 'iris_133': 6.3, 'iris_134': 6.1, 'iris_135': 7.7, 'iris_136': 6.3, 'iris_137': 6.4, 'iris_138': 6.0, 'iris_139': 6.9, 'iris_140': 6.7, 'iris_141': 6.9, 'iris_142': 5.8, 'iris_143': 6.8, 'iris_144': 6.7, 'iris_145': 6.7, 'iris_146': 6.3, 'iris_147': 6.5, 'iris_148': 6.2, 'iris_149': 5.9}, 'sepal_width': {'iris_0': 3.5, 'iris_1': 3.0, 'iris_2': 3.2, 'iris_3': 3.1, 'iris_4': 3.6, 'iris_5': 3.9, 'iris_6': 3.4, 'iris_7': 3.4, 'iris_8': 2.9, 'iris_9': 3.1, 'iris_10': 3.7, 'iris_11': 3.4, 'iris_12': 3.0, 'iris_13': 3.0, 'iris_14': 4.0, 'iris_15': 4.4, 'iris_16': 3.9, 'iris_17': 3.5, 'iris_18': 3.8, 'iris_19': 3.8, 'iris_20': 3.4, 'iris_21': 3.7, 'iris_22': 3.6, 'iris_23': 3.3, 'iris_24': 3.4, 'iris_25': 3.0, 'iris_26': 3.4, 'iris_27': 3.5, 'iris_28': 3.4, 'iris_29': 3.2, 'iris_30': 3.1, 'iris_31': 3.4, 'iris_32': 4.1, 'iris_33': 4.2, 'iris_34': 3.1, 'iris_35': 3.2, 'iris_36': 3.5, 'iris_37': 3.6, 'iris_38': 3.0, 'iris_39': 3.4, 'iris_40': 3.5, 'iris_41': 2.3, 'iris_42': 3.2, 'iris_43': 3.5, 'iris_44': 3.8, 'iris_45': 3.0, 'iris_46': 3.8, 'iris_47': 3.2, 'iris_48': 3.7, 'iris_49': 3.3, 'iris_50': 3.2, 'iris_51': 3.2, 'iris_52': 3.1, 'iris_53': 2.3, 'iris_54': 2.8, 'iris_55': 2.8, 'iris_56': 3.3, 'iris_57': 2.4, 'iris_58': 2.9, 'iris_59': 2.7, 'iris_60': 2.0, 'iris_61': 3.0, 'iris_62': 2.2, 'iris_63': 2.9, 'iris_64': 2.9, 'iris_65': 3.1, 'iris_66': 3.0, 'iris_67': 2.7, 'iris_68': 2.2, 'iris_69': 2.5, 'iris_70': 3.2, 'iris_71': 2.8, 'iris_72': 2.5, 'iris_73': 2.8, 'iris_74': 2.9, 'iris_75': 3.0, 'iris_76': 2.8, 'iris_77': 3.0, 'iris_78': 2.9, 'iris_79': 2.6, 'iris_80': 2.4, 'iris_81': 2.4, 'iris_82': 2.7, 'iris_83': 2.7, 'iris_84': 3.0, 'iris_85': 3.4, 'iris_86': 3.1, 'iris_87': 2.3, 'iris_88': 3.0, 'iris_89': 2.5, 'iris_90': 2.6, 'iris_91': 3.0, 'iris_92': 2.6, 'iris_93': 2.3, 'iris_94': 2.7, 'iris_95': 3.0, 'iris_96': 2.9, 'iris_97': 2.9, 'iris_98': 2.5, 'iris_99': 2.8, 'iris_100': 3.3, 'iris_101': 2.7, 'iris_102': 3.0, 'iris_103': 2.9, 'iris_104': 3.0, 'iris_105': 3.0, 'iris_106': 2.5, 'iris_107': 2.9, 'iris_108': 2.5, 'iris_109': 3.6, 'iris_110': 3.2, 'iris_111': 2.7, 'iris_112': 3.0, 'iris_113': 2.5, 'iris_114': 2.8, 'iris_115': 3.2, 'iris_116': 3.0, 'iris_117': 3.8, 'iris_118': 2.6, 'iris_119': 2.2, 'iris_120': 3.2, 'iris_121': 2.8, 'iris_122': 2.8, 'iris_123': 2.7, 'iris_124': 3.3, 'iris_125': 3.2, 'iris_126': 2.8, 'iris_127': 3.0, 'iris_128': 2.8, 'iris_129': 3.0, 'iris_130': 2.8, 'iris_131': 3.8, 'iris_132': 2.8, 'iris_133': 2.8, 'iris_134': 2.6, 'iris_135': 3.0, 'iris_136': 3.4, 'iris_137': 3.1, 'iris_138': 3.0, 'iris_139': 3.1, 'iris_140': 3.1, 'iris_141': 3.1, 'iris_142': 2.7, 'iris_143': 3.2, 'iris_144': 3.3, 'iris_145': 3.0, 'iris_146': 2.5, 'iris_147': 3.0, 'iris_148': 3.4, 'iris_149': 3.0}, 'petal_length': {'iris_0': 1.4, 'iris_1': 1.4, 'iris_2': 1.3, 'iris_3': 1.5, 'iris_4': 1.4, 'iris_5': 1.7, 'iris_6': 1.4, 'iris_7': 1.5, 'iris_8': 1.4, 'iris_9': 1.5, 'iris_10': 1.5, 'iris_11': 1.6, 'iris_12': 1.4, 'iris_13': 1.1, 'iris_14': 1.2, 'iris_15': 1.5, 'iris_16': 1.3, 'iris_17': 1.4, 'iris_18': 1.7, 'iris_19': 1.5, 'iris_20': 1.7, 'iris_21': 1.5, 'iris_22': 1.0, 'iris_23': 1.7, 'iris_24': 1.9, 'iris_25': 1.6, 'iris_26': 1.6, 'iris_27': 1.5, 'iris_28': 1.4, 'iris_29': 1.6, 'iris_30': 1.6, 'iris_31': 1.5, 'iris_32': 1.5, 'iris_33': 1.4, 'iris_34': 1.5, 'iris_35': 1.2, 'iris_36': 1.3, 'iris_37': 1.4, 'iris_38': 1.3, 'iris_39': 1.5, 'iris_40': 1.3, 'iris_41': 1.3, 'iris_42': 1.3, 'iris_43': 1.6, 'iris_44': 1.9, 'iris_45': 1.4, 'iris_46': 1.6, 'iris_47': 1.4, 'iris_48': 1.5, 'iris_49': 1.4, 'iris_50': 4.7, 'iris_51': 4.5, 'iris_52': 4.9, 'iris_53': 4.0, 'iris_54': 4.6, 'iris_55': 4.5, 'iris_56': 4.7, 'iris_57': 3.3, 'iris_58': 4.6, 'iris_59': 3.9, 'iris_60': 3.5, 'iris_61': 4.2, 'iris_62': 4.0, 'iris_63': 4.7, 'iris_64': 3.6, 'iris_65': 4.4, 'iris_66': 4.5, 'iris_67': 4.1, 'iris_68': 4.5, 'iris_69': 3.9, 'iris_70': 4.8, 'iris_71': 4.0, 'iris_72': 4.9, 'iris_73': 4.7, 'iris_74': 4.3, 'iris_75': 4.4, 'iris_76': 4.8, 'iris_77': 5.0, 'iris_78': 4.5, 'iris_79': 3.5, 'iris_80': 3.8, 'iris_81': 3.7, 'iris_82': 3.9, 'iris_83': 5.1, 'iris_84': 4.5, 'iris_85': 4.5, 'iris_86': 4.7, 'iris_87': 4.4, 'iris_88': 4.1, 'iris_89': 4.0, 'iris_90': 4.4, 'iris_91': 4.6, 'iris_92': 4.0, 'iris_93': 3.3, 'iris_94': 4.2, 'iris_95': 4.2, 'iris_96': 4.2, 'iris_97': 4.3, 'iris_98': 3.0, 'iris_99': 4.1, 'iris_100': 6.0, 'iris_101': 5.1, 'iris_102': 5.9, 'iris_103': 5.6, 'iris_104': 5.8, 'iris_105': 6.6, 'iris_106': 4.5, 'iris_107': 6.3, 'iris_108': 5.8, 'iris_109': 6.1, 'iris_110': 5.1, 'iris_111': 5.3, 'iris_112': 5.5, 'iris_113': 5.0, 'iris_114': 5.1, 'iris_115': 5.3, 'iris_116': 5.5, 'iris_117': 6.7, 'iris_118': 6.9, 'iris_119': 5.0, 'iris_120': 5.7, 'iris_121': 4.9, 'iris_122': 6.7, 'iris_123': 4.9, 'iris_124': 5.7, 'iris_125': 6.0, 'iris_126': 4.8, 'iris_127': 4.9, 'iris_128': 5.6, 'iris_129': 5.8, 'iris_130': 6.1, 'iris_131': 6.4, 'iris_132': 5.6, 'iris_133': 5.1, 'iris_134': 5.6, 'iris_135': 6.1, 'iris_136': 5.6, 'iris_137': 5.5, 'iris_138': 4.8, 'iris_139': 5.4, 'iris_140': 5.6, 'iris_141': 5.1, 'iris_142': 5.1, 'iris_143': 5.9, 'iris_144': 5.7, 'iris_145': 5.2, 'iris_146': 5.0, 'iris_147': 5.2, 'iris_148': 5.4, 'iris_149': 5.1}, 'petal_width': {'iris_0': 0.2, 'iris_1': 0.2, 'iris_2': 0.2, 'iris_3': 0.2, 'iris_4': 0.2, 'iris_5': 0.4, 'iris_6': 0.3, 'iris_7': 0.2, 'iris_8': 0.2, 'iris_9': 0.1, 'iris_10': 0.2, 'iris_11': 0.2, 'iris_12': 0.1, 'iris_13': 0.1, 'iris_14': 0.2, 'iris_15': 0.4, 'iris_16': 0.4, 'iris_17': 0.3, 'iris_18': 0.3, 'iris_19': 0.3, 'iris_20': 0.2, 'iris_21': 0.4, 'iris_22': 0.2, 'iris_23': 0.5, 'iris_24': 0.2, 'iris_25': 0.2, 'iris_26': 0.4, 'iris_27': 0.2, 'iris_28': 0.2, 'iris_29': 0.2, 'iris_30': 0.2, 'iris_31': 0.4, 'iris_32': 0.1, 'iris_33': 0.2, 'iris_34': 0.2, 'iris_35': 0.2, 'iris_36': 0.2, 'iris_37': 0.1, 'iris_38': 0.2, 'iris_39': 0.2, 'iris_40': 0.3, 'iris_41': 0.3, 'iris_42': 0.2, 'iris_43': 0.6, 'iris_44': 0.4, 'iris_45': 0.3, 'iris_46': 0.2, 'iris_47': 0.2, 'iris_48': 0.2, 'iris_49': 0.2, 'iris_50': 1.4, 'iris_51': 1.5, 'iris_52': 1.5, 'iris_53': 1.3, 'iris_54': 1.5, 'iris_55': 1.3, 'iris_56': 1.6, 'iris_57': 1.0, 'iris_58': 1.3, 'iris_59': 1.4, 'iris_60': 1.0, 'iris_61': 1.5, 'iris_62': 1.0, 'iris_63': 1.4, 'iris_64': 1.3, 'iris_65': 1.4, 'iris_66': 1.5, 'iris_67': 1.0, 'iris_68': 1.5, 'iris_69': 1.1, 'iris_70': 1.8, 'iris_71': 1.3, 'iris_72': 1.5, 'iris_73': 1.2, 'iris_74': 1.3, 'iris_75': 1.4, 'iris_76': 1.4, 'iris_77': 1.7, 'iris_78': 1.5, 'iris_79': 1.0, 'iris_80': 1.1, 'iris_81': 1.0, 'iris_82': 1.2, 'iris_83': 1.6, 'iris_84': 1.5, 'iris_85': 1.6, 'iris_86': 1.5, 'iris_87': 1.3, 'iris_88': 1.3, 'iris_89': 1.3, 'iris_90': 1.2, 'iris_91': 1.4, 'iris_92': 1.2, 'iris_93': 1.0, 'iris_94': 1.3, 'iris_95': 1.2, 'iris_96': 1.3, 'iris_97': 1.3, 'iris_98': 1.1, 'iris_99': 1.3, 'iris_100': 2.5, 'iris_101': 1.9, 'iris_102': 2.1, 'iris_103': 1.8, 'iris_104': 2.2, 'iris_105': 2.1, 'iris_106': 1.7, 'iris_107': 1.8, 'iris_108': 1.8, 'iris_109': 2.5, 'iris_110': 2.0, 'iris_111': 1.9, 'iris_112': 2.1, 'iris_113': 2.0, 'iris_114': 2.4, 'iris_115': 2.3, 'iris_116': 1.8, 'iris_117': 2.2, 'iris_118': 2.3, 'iris_119': 1.5, 'iris_120': 2.3, 'iris_121': 2.0, 'iris_122': 2.0, 'iris_123': 1.8, 'iris_124': 2.1, 'iris_125': 1.8, 'iris_126': 1.8, 'iris_127': 1.8, 'iris_128': 2.1, 'iris_129': 1.6, 'iris_130': 1.9, 'iris_131': 2.0, 'iris_132': 2.2, 'iris_133': 1.5, 'iris_134': 1.4, 'iris_135': 2.3, 'iris_136': 2.4, 'iris_137': 1.8, 'iris_138': 1.8, 'iris_139': 2.1, 'iris_140': 2.4, 'iris_141': 2.3, 'iris_142': 1.9, 'iris_143': 2.3, 'iris_144': 2.5, 'iris_145': 2.3, 'iris_146': 1.9, 'iris_147': 2.0, 'iris_148': 2.3, 'iris_149': 1.8}})
y_iris = pd.Series({'iris_0': 'setosa', 'iris_1': 'setosa', 'iris_2': 'setosa', 'iris_3': 'setosa', 'iris_4': 'setosa', 'iris_5': 'setosa', 'iris_6': 'setosa', 'iris_7': 'setosa', 'iris_8': 'setosa', 'iris_9': 'setosa', 'iris_10': 'setosa', 'iris_11': 'setosa', 'iris_12': 'setosa', 'iris_13': 'setosa', 'iris_14': 'setosa', 'iris_15': 'setosa', 'iris_16': 'setosa', 'iris_17': 'setosa', 'iris_18': 'setosa', 'iris_19': 'setosa', 'iris_20': 'setosa', 'iris_21': 'setosa', 'iris_22': 'setosa', 'iris_23': 'setosa', 'iris_24': 'setosa', 'iris_25': 'setosa', 'iris_26': 'setosa', 'iris_27': 'setosa', 'iris_28': 'setosa', 'iris_29': 'setosa', 'iris_30': 'setosa', 'iris_31': 'setosa', 'iris_32': 'setosa', 'iris_33': 'setosa', 'iris_34': 'setosa', 'iris_35': 'setosa', 'iris_36': 'setosa', 'iris_37': 'setosa', 'iris_38': 'setosa', 'iris_39': 'setosa', 'iris_40': 'setosa', 'iris_41': 'setosa', 'iris_42': 'setosa', 'iris_43': 'setosa', 'iris_44': 'setosa', 'iris_45': 'setosa', 'iris_46': 'setosa', 'iris_47': 'setosa', 'iris_48': 'setosa', 'iris_49': 'setosa', 'iris_50': 'versicolor', 'iris_51': 'versicolor', 'iris_52': 'versicolor', 'iris_53': 'versicolor', 'iris_54': 'versicolor', 'iris_55': 'versicolor', 'iris_56': 'versicolor', 'iris_57': 'versicolor', 'iris_58': 'versicolor', 'iris_59': 'versicolor', 'iris_60': 'versicolor', 'iris_61': 'versicolor', 'iris_62': 'versicolor', 'iris_63': 'versicolor', 'iris_64': 'versicolor', 'iris_65': 'versicolor', 'iris_66': 'versicolor', 'iris_67': 'versicolor', 'iris_68': 'versicolor', 'iris_69': 'versicolor', 'iris_70': 'versicolor', 'iris_71': 'versicolor', 'iris_72': 'versicolor', 'iris_73': 'versicolor', 'iris_74': 'versicolor', 'iris_75': 'versicolor', 'iris_76': 'versicolor', 'iris_77': 'versicolor', 'iris_78': 'versicolor', 'iris_79': 'versicolor', 'iris_80': 'versicolor', 'iris_81': 'versicolor', 'iris_82': 'versicolor', 'iris_83': 'versicolor', 'iris_84': 'versicolor', 'iris_85': 'versicolor', 'iris_86': 'versicolor', 'iris_87': 'versicolor', 'iris_88': 'versicolor', 'iris_89': 'versicolor', 'iris_90': 'versicolor', 'iris_91': 'versicolor', 'iris_92': 'versicolor', 'iris_93': 'versicolor', 'iris_94': 'versicolor', 'iris_95': 'versicolor', 'iris_96': 'versicolor', 'iris_97': 'versicolor', 'iris_98': 'versicolor', 'iris_99': 'versicolor', 'iris_100': 'virginica', 'iris_101': 'virginica', 'iris_102': 'virginica', 'iris_103': 'virginica', 'iris_104': 'virginica', 'iris_105': 'virginica', 'iris_106': 'virginica', 'iris_107': 'virginica', 'iris_108': 'virginica', 'iris_109': 'virginica', 'iris_110': 'virginica', 'iris_111': 'virginica', 'iris_112': 'virginica', 'iris_113': 'virginica', 'iris_114': 'virginica', 'iris_115': 'virginica', 'iris_116': 'virginica', 'iris_117': 'virginica', 'iris_118': 'virginica', 'iris_119': 'virginica', 'iris_120': 'virginica', 'iris_121': 'virginica', 'iris_122': 'virginica', 'iris_123': 'virginica', 'iris_124': 'virginica', 'iris_125': 'virginica', 'iris_126': 'virginica', 'iris_127': 'virginica', 'iris_128': 'virginica', 'iris_129': 'virginica', 'iris_130': 'virginica', 'iris_131': 'virginica', 'iris_132': 'virginica', 'iris_133': 'virginica', 'iris_134': 'virginica', 'iris_135': 'virginica', 'iris_136': 'virginica', 'iris_137': 'virginica', 'iris_138': 'virginica', 'iris_139': 'virginica', 'iris_140': 'virginica', 'iris_141': 'virginica', 'iris_142': 'virginica', 'iris_143': 'virginica', 'iris_144': 'virginica', 'iris_145': 'virginica', 'iris_146': 'virginica', 'iris_147': 'virginica', 'iris_148': 'virginica', 'iris_149': 'virginica'})
c_iris = pd.Series({'iris_0': '#db5f57', 'iris_1': '#db5f57', 'iris_2': '#db5f57', 'iris_3': '#db5f57', 'iris_4': '#db5f57', 'iris_5': '#db5f57', 'iris_6': '#db5f57', 'iris_7': '#db5f57', 'iris_8': '#db5f57', 'iris_9': '#db5f57', 'iris_10': '#db5f57', 'iris_11': '#db5f57', 'iris_12': '#db5f57', 'iris_13': '#db5f57', 'iris_14': '#db5f57', 'iris_15': '#db5f57', 'iris_16': '#db5f57', 'iris_17': '#db5f57', 'iris_18': '#db5f57', 'iris_19': '#db5f57', 'iris_20': '#db5f57', 'iris_21': '#db5f57', 'iris_22': '#db5f57', 'iris_23': '#db5f57', 'iris_24': '#db5f57', 'iris_25': '#db5f57', 'iris_26': '#db5f57', 'iris_27': '#db5f57', 'iris_28': '#db5f57', 'iris_29': '#db5f57', 'iris_30': '#db5f57', 'iris_31': '#db5f57', 'iris_32': '#db5f57', 'iris_33': '#db5f57', 'iris_34': '#db5f57', 'iris_35': '#db5f57', 'iris_36': '#db5f57', 'iris_37': '#db5f57', 'iris_38': '#db5f57', 'iris_39': '#db5f57', 'iris_40': '#db5f57', 'iris_41': '#db5f57', 'iris_42': '#db5f57', 'iris_43': '#db5f57', 'iris_44': '#db5f57', 'iris_45': '#db5f57', 'iris_46': '#db5f57', 'iris_47': '#db5f57', 'iris_48': '#db5f57', 'iris_49': '#db5f57', 'iris_50': '#57db5f', 'iris_51': '#57db5f', 'iris_52': '#57db5f', 'iris_53': '#57db5f', 'iris_54': '#57db5f', 'iris_55': '#57db5f', 'iris_56': '#57db5f', 'iris_57': '#57db5f', 'iris_58': '#57db5f', 'iris_59': '#57db5f', 'iris_60': '#57db5f', 'iris_61': '#57db5f', 'iris_62': '#57db5f', 'iris_63': '#57db5f', 'iris_64': '#57db5f', 'iris_65': '#57db5f', 'iris_66': '#57db5f', 'iris_67': '#57db5f', 'iris_68': '#57db5f', 'iris_69': '#57db5f', 'iris_70': '#57db5f', 'iris_71': '#57db5f', 'iris_72': '#57db5f', 'iris_73': '#57db5f', 'iris_74': '#57db5f', 'iris_75': '#57db5f', 'iris_76': '#57db5f', 'iris_77': '#57db5f', 'iris_78': '#57db5f', 'iris_79': '#57db5f', 'iris_80': '#57db5f', 'iris_81': '#57db5f', 'iris_82': '#57db5f', 'iris_83': '#57db5f', 'iris_84': '#57db5f', 'iris_85': '#57db5f', 'iris_86': '#57db5f', 'iris_87': '#57db5f', 'iris_88': '#57db5f', 'iris_89': '#57db5f', 'iris_90': '#57db5f', 'iris_91': '#57db5f', 'iris_92': '#57db5f', 'iris_93': '#57db5f', 'iris_94': '#57db5f', 'iris_95': '#57db5f', 'iris_96': '#57db5f', 'iris_97': '#57db5f', 'iris_98': '#57db5f', 'iris_99': '#57db5f', 'iris_100': '#5f57db', 'iris_101': '#5f57db', 'iris_102': '#5f57db', 'iris_103': '#5f57db', 'iris_104': '#5f57db', 'iris_105': '#5f57db', 'iris_106': '#5f57db', 'iris_107': '#5f57db', 'iris_108': '#5f57db', 'iris_109': '#5f57db', 'iris_110': '#5f57db', 'iris_111': '#5f57db', 'iris_112': '#5f57db', 'iris_113': '#5f57db', 'iris_114': '#5f57db', 'iris_115': '#5f57db', 'iris_116': '#5f57db', 'iris_117': '#5f57db', 'iris_118': '#5f57db', 'iris_119': '#5f57db', 'iris_120': '#5f57db', 'iris_121': '#5f57db', 'iris_122': '#5f57db', 'iris_123': '#5f57db', 'iris_124': '#5f57db', 'iris_125': '#5f57db', 'iris_126': '#5f57db', 'iris_127': '#5f57db', 'iris_128': '#5f57db', 'iris_129': '#5f57db', 'iris_130': '#5f57db', 'iris_131': '#5f57db', 'iris_132': '#5f57db', 'iris_133': '#5f57db', 'iris_134': '#5f57db', 'iris_135': '#5f57db', 'iris_136': '#5f57db', 'iris_137': '#5f57db', 'iris_138': '#5f57db', 'iris_139': '#5f57db', 'iris_140': '#5f57db', 'iris_141': '#5f57db', 'iris_142': '#5f57db', 'iris_143': '#5f57db', 'iris_144': '#5f57db', 'iris_145': '#5f57db', 'iris_146': '#5f57db', 'iris_147': '#5f57db', 'iris_148': '#5f57db', 'iris_149': '#5f57db'})

# Connections
df_dense = X_iris.T.corr("pearson")
y_condensed = defaultdict(dict)
for edge, w in df_dense.unstack().items():
    y_condensed[frozenset(edge)] = w
y_condensed = pd.Series(y_condensed)
tol_connection = 0.9

# Graph
graph = nx.Graph()
for edge, w in y_condensed[lambda x: x > tol_connection].items():
    if len(edge) == 1:
        node = list(edge)[0]
        edge = (node, node)
    graph.add_edge(*edge, weight=abs(w))

# Plot network
nodes = list(graph.nodes())
weights = np.asarray(list(map(lambda x: x[-1]["weight"], graph.edges(data=True))))**2

pos = dict(zip(nodes, PCA(n_components=2, random_state=0).fit_transform(X_iris.loc[nodes])))
with plt.style.context("seaborn-white"):
    fig, ax = plt.subplots(figsize=(8,8))
    nx.draw_networkx_nodes(graph, pos=pos, node_color=c_iris[nodes], ax=ax, node_size=100, edgecolors="white", linewidths=1)
    nx.draw_networkx_edges(graph, pos=pos, width=weights, alpha=0.1618, ax=ax)

Here is an example of edge bundling from datashader (not for this graph). Aside from doing this in datashader and then loading the image separately, how could this be done in matplotlib?

Answer 1

This can be done fairly easily for matplotlib using hammer_bundle from datashader. Datashader is a python library which uses a lot of pandas DataFrames, so getting the data into a format for matplotlib is fairly easy. (I assume the main goal is to plot this easily in matplotlib, but if one really doesn't want to install datashader, the hammer_bundle file from datashader could be used separately without a full install.)

hammer_bundle wants two dataframes, one for the nodes (with columns=['name', 'x', 'y']) and one for the edges (with columns=['x', 'y']) -- at least these specifics work, and I don't know fully what flexibility exists).

I'll include the original code for completeness, where all I've added is a couple of imports:

from sklearn.decomposition import PCA
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
from collections import defaultdict
from datashader.bundling import hammer_bundle


# Data
X_iris = pd.DataFrame({'sepal_length': {'iris_0': 5.1, 'iris_1': 4.9, 'iris_2': 4.7, 'iris_3': 4.6, 'iris_4': 5.0, 'iris_5': 5.4, 'iris_6': 4.6, 'iris_7': 5.0, 'iris_8': 4.4, 'iris_9': 4.9, 'iris_10': 5.4, 'iris_11': 4.8, 'iris_12': 4.8, 'iris_13': 4.3, 'iris_14': 5.8, 'iris_15': 5.7, 'iris_16': 5.4, 'iris_17': 5.1, 'iris_18': 5.7, 'iris_19': 5.1, 'iris_20': 5.4, 'iris_21': 5.1, 'iris_22': 4.6, 'iris_23': 5.1, 'iris_24': 4.8, 'iris_25': 5.0, 'iris_26': 5.0, 'iris_27': 5.2, 'iris_28': 5.2, 'iris_29': 4.7, 'iris_30': 4.8, 'iris_31': 5.4, 'iris_32': 5.2, 'iris_33': 5.5, 'iris_34': 4.9, 'iris_35': 5.0, 'iris_36': 5.5, 'iris_37': 4.9, 'iris_38': 4.4, 'iris_39': 5.1, 'iris_40': 5.0, 'iris_41': 4.5, 'iris_42': 4.4, 'iris_43': 5.0, 'iris_44': 5.1, 'iris_45': 4.8, 'iris_46': 5.1, 'iris_47': 4.6, 'iris_48': 5.3, 'iris_49': 5.0, 'iris_50': 7.0, 'iris_51': 6.4, 'iris_52': 6.9, 'iris_53': 5.5, 'iris_54': 6.5, 'iris_55': 5.7, 'iris_56': 6.3, 'iris_57': 4.9, 'iris_58': 6.6, 'iris_59': 5.2, 'iris_60': 5.0, 'iris_61': 5.9, 'iris_62': 6.0, 'iris_63': 6.1, 'iris_64': 5.6, 'iris_65': 6.7, 'iris_66': 5.6, 'iris_67': 5.8, 'iris_68': 6.2, 'iris_69': 5.6, 'iris_70': 5.9, 'iris_71': 6.1, 'iris_72': 6.3, 'iris_73': 6.1, 'iris_74': 6.4, 'iris_75': 6.6, 'iris_76': 6.8, 'iris_77': 6.7, 'iris_78': 6.0, 'iris_79': 5.7, 'iris_80': 5.5, 'iris_81': 5.5, 'iris_82': 5.8, 'iris_83': 6.0, 'iris_84': 5.4, 'iris_85': 6.0, 'iris_86': 6.7, 'iris_87': 6.3, 'iris_88': 5.6, 'iris_89': 5.5, 'iris_90': 5.5, 'iris_91': 6.1, 'iris_92': 5.8, 'iris_93': 5.0, 'iris_94': 5.6, 'iris_95': 5.7, 'iris_96': 5.7, 'iris_97': 6.2, 'iris_98': 5.1, 'iris_99': 5.7, 'iris_100': 6.3, 'iris_101': 5.8, 'iris_102': 7.1, 'iris_103': 6.3, 'iris_104': 6.5, 'iris_105': 7.6, 'iris_106': 4.9, 'iris_107': 7.3, 'iris_108': 6.7, 'iris_109': 7.2, 'iris_110': 6.5, 'iris_111': 6.4, 'iris_112': 6.8, 'iris_113': 5.7, 'iris_114': 5.8, 'iris_115': 6.4, 'iris_116': 6.5, 'iris_117': 7.7, 'iris_118': 7.7, 'iris_119': 6.0, 'iris_120': 6.9, 'iris_121': 5.6, 'iris_122': 7.7, 'iris_123': 6.3, 'iris_124': 6.7, 'iris_125': 7.2, 'iris_126': 6.2, 'iris_127': 6.1, 'iris_128': 6.4, 'iris_129': 7.2, 'iris_130': 7.4, 'iris_131': 7.9, 'iris_132': 6.4, 'iris_133': 6.3, 'iris_134': 6.1, 'iris_135': 7.7, 'iris_136': 6.3, 'iris_137': 6.4, 'iris_138': 6.0, 'iris_139': 6.9, 'iris_140': 6.7, 'iris_141': 6.9, 'iris_142': 5.8, 'iris_143': 6.8, 'iris_144': 6.7, 'iris_145': 6.7, 'iris_146': 6.3, 'iris_147': 6.5, 'iris_148': 6.2, 'iris_149': 5.9}, 'sepal_width': {'iris_0': 3.5, 'iris_1': 3.0, 'iris_2': 3.2, 'iris_3': 3.1, 'iris_4': 3.6, 'iris_5': 3.9, 'iris_6': 3.4, 'iris_7': 3.4, 'iris_8': 2.9, 'iris_9': 3.1, 'iris_10': 3.7, 'iris_11': 3.4, 'iris_12': 3.0, 'iris_13': 3.0, 'iris_14': 4.0, 'iris_15': 4.4, 'iris_16': 3.9, 'iris_17': 3.5, 'iris_18': 3.8, 'iris_19': 3.8, 'iris_20': 3.4, 'iris_21': 3.7, 'iris_22': 3.6, 'iris_23': 3.3, 'iris_24': 3.4, 'iris_25': 3.0, 'iris_26': 3.4, 'iris_27': 3.5, 'iris_28': 3.4, 'iris_29': 3.2, 'iris_30': 3.1, 'iris_31': 3.4, 'iris_32': 4.1, 'iris_33': 4.2, 'iris_34': 3.1, 'iris_35': 3.2, 'iris_36': 3.5, 'iris_37': 3.6, 'iris_38': 3.0, 'iris_39': 3.4, 'iris_40': 3.5, 'iris_41': 2.3, 'iris_42': 3.2, 'iris_43': 3.5, 'iris_44': 3.8, 'iris_45': 3.0, 'iris_46': 3.8, 'iris_47': 3.2, 'iris_48': 3.7, 'iris_49': 3.3, 'iris_50': 3.2, 'iris_51': 3.2, 'iris_52': 3.1, 'iris_53': 2.3, 'iris_54': 2.8, 'iris_55': 2.8, 'iris_56': 3.3, 'iris_57': 2.4, 'iris_58': 2.9, 'iris_59': 2.7, 'iris_60': 2.0, 'iris_61': 3.0, 'iris_62': 2.2, 'iris_63': 2.9, 'iris_64': 2.9, 'iris_65': 3.1, 'iris_66': 3.0, 'iris_67': 2.7, 'iris_68': 2.2, 'iris_69': 2.5, 'iris_70': 3.2, 'iris_71': 2.8, 'iris_72': 2.5, 'iris_73': 2.8, 'iris_74': 2.9, 'iris_75': 3.0, 'iris_76': 2.8, 'iris_77': 3.0, 'iris_78': 2.9, 'iris_79': 2.6, 'iris_80': 2.4, 'iris_81': 2.4, 'iris_82': 2.7, 'iris_83': 2.7, 'iris_84': 3.0, 'iris_85': 3.4, 'iris_86': 3.1, 'iris_87': 2.3, 'iris_88': 3.0, 'iris_89': 2.5, 'iris_90': 2.6, 'iris_91': 3.0, 'iris_92': 2.6, 'iris_93': 2.3, 'iris_94': 2.7, 'iris_95': 3.0, 'iris_96': 2.9, 'iris_97': 2.9, 'iris_98': 2.5, 'iris_99': 2.8, 'iris_100': 3.3, 'iris_101': 2.7, 'iris_102': 3.0, 'iris_103': 2.9, 'iris_104': 3.0, 'iris_105': 3.0, 'iris_106': 2.5, 'iris_107': 2.9, 'iris_108': 2.5, 'iris_109': 3.6, 'iris_110': 3.2, 'iris_111': 2.7, 'iris_112': 3.0, 'iris_113': 2.5, 'iris_114': 2.8, 'iris_115': 3.2, 'iris_116': 3.0, 'iris_117': 3.8, 'iris_118': 2.6, 'iris_119': 2.2, 'iris_120': 3.2, 'iris_121': 2.8, 'iris_122': 2.8, 'iris_123': 2.7, 'iris_124': 3.3, 'iris_125': 3.2, 'iris_126': 2.8, 'iris_127': 3.0, 'iris_128': 2.8, 'iris_129': 3.0, 'iris_130': 2.8, 'iris_131': 3.8, 'iris_132': 2.8, 'iris_133': 2.8, 'iris_134': 2.6, 'iris_135': 3.0, 'iris_136': 3.4, 'iris_137': 3.1, 'iris_138': 3.0, 'iris_139': 3.1, 'iris_140': 3.1, 'iris_141': 3.1, 'iris_142': 2.7, 'iris_143': 3.2, 'iris_144': 3.3, 'iris_145': 3.0, 'iris_146': 2.5, 'iris_147': 3.0, 'iris_148': 3.4, 'iris_149': 3.0}, 'petal_length': {'iris_0': 1.4, 'iris_1': 1.4, 'iris_2': 1.3, 'iris_3': 1.5, 'iris_4': 1.4, 'iris_5': 1.7, 'iris_6': 1.4, 'iris_7': 1.5, 'iris_8': 1.4, 'iris_9': 1.5, 'iris_10': 1.5, 'iris_11': 1.6, 'iris_12': 1.4, 'iris_13': 1.1, 'iris_14': 1.2, 'iris_15': 1.5, 'iris_16': 1.3, 'iris_17': 1.4, 'iris_18': 1.7, 'iris_19': 1.5, 'iris_20': 1.7, 'iris_21': 1.5, 'iris_22': 1.0, 'iris_23': 1.7, 'iris_24': 1.9, 'iris_25': 1.6, 'iris_26': 1.6, 'iris_27': 1.5, 'iris_28': 1.4, 'iris_29': 1.6, 'iris_30': 1.6, 'iris_31': 1.5, 'iris_32': 1.5, 'iris_33': 1.4, 'iris_34': 1.5, 'iris_35': 1.2, 'iris_36': 1.3, 'iris_37': 1.4, 'iris_38': 1.3, 'iris_39': 1.5, 'iris_40': 1.3, 'iris_41': 1.3, 'iris_42': 1.3, 'iris_43': 1.6, 'iris_44': 1.9, 'iris_45': 1.4, 'iris_46': 1.6, 'iris_47': 1.4, 'iris_48': 1.5, 'iris_49': 1.4, 'iris_50': 4.7, 'iris_51': 4.5, 'iris_52': 4.9, 'iris_53': 4.0, 'iris_54': 4.6, 'iris_55': 4.5, 'iris_56': 4.7, 'iris_57': 3.3, 'iris_58': 4.6, 'iris_59': 3.9, 'iris_60': 3.5, 'iris_61': 4.2, 'iris_62': 4.0, 'iris_63': 4.7, 'iris_64': 3.6, 'iris_65': 4.4, 'iris_66': 4.5, 'iris_67': 4.1, 'iris_68': 4.5, 'iris_69': 3.9, 'iris_70': 4.8, 'iris_71': 4.0, 'iris_72': 4.9, 'iris_73': 4.7, 'iris_74': 4.3, 'iris_75': 4.4, 'iris_76': 4.8, 'iris_77': 5.0, 'iris_78': 4.5, 'iris_79': 3.5, 'iris_80': 3.8, 'iris_81': 3.7, 'iris_82': 3.9, 'iris_83': 5.1, 'iris_84': 4.5, 'iris_85': 4.5, 'iris_86': 4.7, 'iris_87': 4.4, 'iris_88': 4.1, 'iris_89': 4.0, 'iris_90': 4.4, 'iris_91': 4.6, 'iris_92': 4.0, 'iris_93': 3.3, 'iris_94': 4.2, 'iris_95': 4.2, 'iris_96': 4.2, 'iris_97': 4.3, 'iris_98': 3.0, 'iris_99': 4.1, 'iris_100': 6.0, 'iris_101': 5.1, 'iris_102': 5.9, 'iris_103': 5.6, 'iris_104': 5.8, 'iris_105': 6.6, 'iris_106': 4.5, 'iris_107': 6.3, 'iris_108': 5.8, 'iris_109': 6.1, 'iris_110': 5.1, 'iris_111': 5.3, 'iris_112': 5.5, 'iris_113': 5.0, 'iris_114': 5.1, 'iris_115': 5.3, 'iris_116': 5.5, 'iris_117': 6.7, 'iris_118': 6.9, 'iris_119': 5.0, 'iris_120': 5.7, 'iris_121': 4.9, 'iris_122': 6.7, 'iris_123': 4.9, 'iris_124': 5.7, 'iris_125': 6.0, 'iris_126': 4.8, 'iris_127': 4.9, 'iris_128': 5.6, 'iris_129': 5.8, 'iris_130': 6.1, 'iris_131': 6.4, 'iris_132': 5.6, 'iris_133': 5.1, 'iris_134': 5.6, 'iris_135': 6.1, 'iris_136': 5.6, 'iris_137': 5.5, 'iris_138': 4.8, 'iris_139': 5.4, 'iris_140': 5.6, 'iris_141': 5.1, 'iris_142': 5.1, 'iris_143': 5.9, 'iris_144': 5.7, 'iris_145': 5.2, 'iris_146': 5.0, 'iris_147': 5.2, 'iris_148': 5.4, 'iris_149': 5.1}, 'petal_width': {'iris_0': 0.2, 'iris_1': 0.2, 'iris_2': 0.2, 'iris_3': 0.2, 'iris_4': 0.2, 'iris_5': 0.4, 'iris_6': 0.3, 'iris_7': 0.2, 'iris_8': 0.2, 'iris_9': 0.1, 'iris_10': 0.2, 'iris_11': 0.2, 'iris_12': 0.1, 'iris_13': 0.1, 'iris_14': 0.2, 'iris_15': 0.4, 'iris_16': 0.4, 'iris_17': 0.3, 'iris_18': 0.3, 'iris_19': 0.3, 'iris_20': 0.2, 'iris_21': 0.4, 'iris_22': 0.2, 'iris_23': 0.5, 'iris_24': 0.2, 'iris_25': 0.2, 'iris_26': 0.4, 'iris_27': 0.2, 'iris_28': 0.2, 'iris_29': 0.2, 'iris_30': 0.2, 'iris_31': 0.4, 'iris_32': 0.1, 'iris_33': 0.2, 'iris_34': 0.2, 'iris_35': 0.2, 'iris_36': 0.2, 'iris_37': 0.1, 'iris_38': 0.2, 'iris_39': 0.2, 'iris_40': 0.3, 'iris_41': 0.3, 'iris_42': 0.2, 'iris_43': 0.6, 'iris_44': 0.4, 'iris_45': 0.3, 'iris_46': 0.2, 'iris_47': 0.2, 'iris_48': 0.2, 'iris_49': 0.2, 'iris_50': 1.4, 'iris_51': 1.5, 'iris_52': 1.5, 'iris_53': 1.3, 'iris_54': 1.5, 'iris_55': 1.3, 'iris_56': 1.6, 'iris_57': 1.0, 'iris_58': 1.3, 'iris_59': 1.4, 'iris_60': 1.0, 'iris_61': 1.5, 'iris_62': 1.0, 'iris_63': 1.4, 'iris_64': 1.3, 'iris_65': 1.4, 'iris_66': 1.5, 'iris_67': 1.0, 'iris_68': 1.5, 'iris_69': 1.1, 'iris_70': 1.8, 'iris_71': 1.3, 'iris_72': 1.5, 'iris_73': 1.2, 'iris_74': 1.3, 'iris_75': 1.4, 'iris_76': 1.4, 'iris_77': 1.7, 'iris_78': 1.5, 'iris_79': 1.0, 'iris_80': 1.1, 'iris_81': 1.0, 'iris_82': 1.2, 'iris_83': 1.6, 'iris_84': 1.5, 'iris_85': 1.6, 'iris_86': 1.5, 'iris_87': 1.3, 'iris_88': 1.3, 'iris_89': 1.3, 'iris_90': 1.2, 'iris_91': 1.4, 'iris_92': 1.2, 'iris_93': 1.0, 'iris_94': 1.3, 'iris_95': 1.2, 'iris_96': 1.3, 'iris_97': 1.3, 'iris_98': 1.1, 'iris_99': 1.3, 'iris_100': 2.5, 'iris_101': 1.9, 'iris_102': 2.1, 'iris_103': 1.8, 'iris_104': 2.2, 'iris_105': 2.1, 'iris_106': 1.7, 'iris_107': 1.8, 'iris_108': 1.8, 'iris_109': 2.5, 'iris_110': 2.0, 'iris_111': 1.9, 'iris_112': 2.1, 'iris_113': 2.0, 'iris_114': 2.4, 'iris_115': 2.3, 'iris_116': 1.8, 'iris_117': 2.2, 'iris_118': 2.3, 'iris_119': 1.5, 'iris_120': 2.3, 'iris_121': 2.0, 'iris_122': 2.0, 'iris_123': 1.8, 'iris_124': 2.1, 'iris_125': 1.8, 'iris_126': 1.8, 'iris_127': 1.8, 'iris_128': 2.1, 'iris_129': 1.6, 'iris_130': 1.9, 'iris_131': 2.0, 'iris_132': 2.2, 'iris_133': 1.5, 'iris_134': 1.4, 'iris_135': 2.3, 'iris_136': 2.4, 'iris_137': 1.8, 'iris_138': 1.8, 'iris_139': 2.1, 'iris_140': 2.4, 'iris_141': 2.3, 'iris_142': 1.9, 'iris_143': 2.3, 'iris_144': 2.5, 'iris_145': 2.3, 'iris_146': 1.9, 'iris_147': 2.0, 'iris_148': 2.3, 'iris_149': 1.8}})
y_iris = pd.Series({'iris_0': 'setosa', 'iris_1': 'setosa', 'iris_2': 'setosa', 'iris_3': 'setosa', 'iris_4': 'setosa', 'iris_5': 'setosa', 'iris_6': 'setosa', 'iris_7': 'setosa', 'iris_8': 'setosa', 'iris_9': 'setosa', 'iris_10': 'setosa', 'iris_11': 'setosa', 'iris_12': 'setosa', 'iris_13': 'setosa', 'iris_14': 'setosa', 'iris_15': 'setosa', 'iris_16': 'setosa', 'iris_17': 'setosa', 'iris_18': 'setosa', 'iris_19': 'setosa', 'iris_20': 'setosa', 'iris_21': 'setosa', 'iris_22': 'setosa', 'iris_23': 'setosa', 'iris_24': 'setosa', 'iris_25': 'setosa', 'iris_26': 'setosa', 'iris_27': 'setosa', 'iris_28': 'setosa', 'iris_29': 'setosa', 'iris_30': 'setosa', 'iris_31': 'setosa', 'iris_32': 'setosa', 'iris_33': 'setosa', 'iris_34': 'setosa', 'iris_35': 'setosa', 'iris_36': 'setosa', 'iris_37': 'setosa', 'iris_38': 'setosa', 'iris_39': 'setosa', 'iris_40': 'setosa', 'iris_41': 'setosa', 'iris_42': 'setosa', 'iris_43': 'setosa', 'iris_44': 'setosa', 'iris_45': 'setosa', 'iris_46': 'setosa', 'iris_47': 'setosa', 'iris_48': 'setosa', 'iris_49': 'setosa', 'iris_50': 'versicolor', 'iris_51': 'versicolor', 'iris_52': 'versicolor', 'iris_53': 'versicolor', 'iris_54': 'versicolor', 'iris_55': 'versicolor', 'iris_56': 'versicolor', 'iris_57': 'versicolor', 'iris_58': 'versicolor', 'iris_59': 'versicolor', 'iris_60': 'versicolor', 'iris_61': 'versicolor', 'iris_62': 'versicolor', 'iris_63': 'versicolor', 'iris_64': 'versicolor', 'iris_65': 'versicolor', 'iris_66': 'versicolor', 'iris_67': 'versicolor', 'iris_68': 'versicolor', 'iris_69': 'versicolor', 'iris_70': 'versicolor', 'iris_71': 'versicolor', 'iris_72': 'versicolor', 'iris_73': 'versicolor', 'iris_74': 'versicolor', 'iris_75': 'versicolor', 'iris_76': 'versicolor', 'iris_77': 'versicolor', 'iris_78': 'versicolor', 'iris_79': 'versicolor', 'iris_80': 'versicolor', 'iris_81': 'versicolor', 'iris_82': 'versicolor', 'iris_83': 'versicolor', 'iris_84': 'versicolor', 'iris_85': 'versicolor', 'iris_86': 'versicolor', 'iris_87': 'versicolor', 'iris_88': 'versicolor', 'iris_89': 'versicolor', 'iris_90': 'versicolor', 'iris_91': 'versicolor', 'iris_92': 'versicolor', 'iris_93': 'versicolor', 'iris_94': 'versicolor', 'iris_95': 'versicolor', 'iris_96': 'versicolor', 'iris_97': 'versicolor', 'iris_98': 'versicolor', 'iris_99': 'versicolor', 'iris_100': 'virginica', 'iris_101': 'virginica', 'iris_102': 'virginica', 'iris_103': 'virginica', 'iris_104': 'virginica', 'iris_105': 'virginica', 'iris_106': 'virginica', 'iris_107': 'virginica', 'iris_108': 'virginica', 'iris_109': 'virginica', 'iris_110': 'virginica', 'iris_111': 'virginica', 'iris_112': 'virginica', 'iris_113': 'virginica', 'iris_114': 'virginica', 'iris_115': 'virginica', 'iris_116': 'virginica', 'iris_117': 'virginica', 'iris_118': 'virginica', 'iris_119': 'virginica', 'iris_120': 'virginica', 'iris_121': 'virginica', 'iris_122': 'virginica', 'iris_123': 'virginica', 'iris_124': 'virginica', 'iris_125': 'virginica', 'iris_126': 'virginica', 'iris_127': 'virginica', 'iris_128': 'virginica', 'iris_129': 'virginica', 'iris_130': 'virginica', 'iris_131': 'virginica', 'iris_132': 'virginica', 'iris_133': 'virginica', 'iris_134': 'virginica', 'iris_135': 'virginica', 'iris_136': 'virginica', 'iris_137': 'virginica', 'iris_138': 'virginica', 'iris_139': 'virginica', 'iris_140': 'virginica', 'iris_141': 'virginica', 'iris_142': 'virginica', 'iris_143': 'virginica', 'iris_144': 'virginica', 'iris_145': 'virginica', 'iris_146': 'virginica', 'iris_147': 'virginica', 'iris_148': 'virginica', 'iris_149': 'virginica'})
c_iris = pd.Series({'iris_0': '#db5f57', 'iris_1': '#db5f57', 'iris_2': '#db5f57', 'iris_3': '#db5f57', 'iris_4': '#db5f57', 'iris_5': '#db5f57', 'iris_6': '#db5f57', 'iris_7': '#db5f57', 'iris_8': '#db5f57', 'iris_9': '#db5f57', 'iris_10': '#db5f57', 'iris_11': '#db5f57', 'iris_12': '#db5f57', 'iris_13': '#db5f57', 'iris_14': '#db5f57', 'iris_15': '#db5f57', 'iris_16': '#db5f57', 'iris_17': '#db5f57', 'iris_18': '#db5f57', 'iris_19': '#db5f57', 'iris_20': '#db5f57', 'iris_21': '#db5f57', 'iris_22': '#db5f57', 'iris_23': '#db5f57', 'iris_24': '#db5f57', 'iris_25': '#db5f57', 'iris_26': '#db5f57', 'iris_27': '#db5f57', 'iris_28': '#db5f57', 'iris_29': '#db5f57', 'iris_30': '#db5f57', 'iris_31': '#db5f57', 'iris_32': '#db5f57', 'iris_33': '#db5f57', 'iris_34': '#db5f57', 'iris_35': '#db5f57', 'iris_36': '#db5f57', 'iris_37': '#db5f57', 'iris_38': '#db5f57', 'iris_39': '#db5f57', 'iris_40': '#db5f57', 'iris_41': '#db5f57', 'iris_42': '#db5f57', 'iris_43': '#db5f57', 'iris_44': '#db5f57', 'iris_45': '#db5f57', 'iris_46': '#db5f57', 'iris_47': '#db5f57', 'iris_48': '#db5f57', 'iris_49': '#db5f57', 'iris_50': '#57db5f', 'iris_51': '#57db5f', 'iris_52': '#57db5f', 'iris_53': '#57db5f', 'iris_54': '#57db5f', 'iris_55': '#57db5f', 'iris_56': '#57db5f', 'iris_57': '#57db5f', 'iris_58': '#57db5f', 'iris_59': '#57db5f', 'iris_60': '#57db5f', 'iris_61': '#57db5f', 'iris_62': '#57db5f', 'iris_63': '#57db5f', 'iris_64': '#57db5f', 'iris_65': '#57db5f', 'iris_66': '#57db5f', 'iris_67': '#57db5f', 'iris_68': '#57db5f', 'iris_69': '#57db5f', 'iris_70': '#57db5f', 'iris_71': '#57db5f', 'iris_72': '#57db5f', 'iris_73': '#57db5f', 'iris_74': '#57db5f', 'iris_75': '#57db5f', 'iris_76': '#57db5f', 'iris_77': '#57db5f', 'iris_78': '#57db5f', 'iris_79': '#57db5f', 'iris_80': '#57db5f', 'iris_81': '#57db5f', 'iris_82': '#57db5f', 'iris_83': '#57db5f', 'iris_84': '#57db5f', 'iris_85': '#57db5f', 'iris_86': '#57db5f', 'iris_87': '#57db5f', 'iris_88': '#57db5f', 'iris_89': '#57db5f', 'iris_90': '#57db5f', 'iris_91': '#57db5f', 'iris_92': '#57db5f', 'iris_93': '#57db5f', 'iris_94': '#57db5f', 'iris_95': '#57db5f', 'iris_96': '#57db5f', 'iris_97': '#57db5f', 'iris_98': '#57db5f', 'iris_99': '#57db5f', 'iris_100': '#5f57db', 'iris_101': '#5f57db', 'iris_102': '#5f57db', 'iris_103': '#5f57db', 'iris_104': '#5f57db', 'iris_105': '#5f57db', 'iris_106': '#5f57db', 'iris_107': '#5f57db', 'iris_108': '#5f57db', 'iris_109': '#5f57db', 'iris_110': '#5f57db', 'iris_111': '#5f57db', 'iris_112': '#5f57db', 'iris_113': '#5f57db', 'iris_114': '#5f57db', 'iris_115': '#5f57db', 'iris_116': '#5f57db', 'iris_117': '#5f57db', 'iris_118': '#5f57db', 'iris_119': '#5f57db', 'iris_120': '#5f57db', 'iris_121': '#5f57db', 'iris_122': '#5f57db', 'iris_123': '#5f57db', 'iris_124': '#5f57db', 'iris_125': '#5f57db', 'iris_126': '#5f57db', 'iris_127': '#5f57db', 'iris_128': '#5f57db', 'iris_129': '#5f57db', 'iris_130': '#5f57db', 'iris_131': '#5f57db', 'iris_132': '#5f57db', 'iris_133': '#5f57db', 'iris_134': '#5f57db', 'iris_135': '#5f57db', 'iris_136': '#5f57db', 'iris_137': '#5f57db', 'iris_138': '#5f57db', 'iris_139': '#5f57db', 'iris_140': '#5f57db', 'iris_141': '#5f57db', 'iris_142': '#5f57db', 'iris_143': '#5f57db', 'iris_144': '#5f57db', 'iris_145': '#5f57db', 'iris_146': '#5f57db', 'iris_147': '#5f57db', 'iris_148': '#5f57db', 'iris_149': '#5f57db'})

# Connections
df_dense = X_iris.T.corr("pearson")
y_condensed = defaultdict(dict)
for edge, w in df_dense.unstack().items():
    y_condensed[frozenset(edge)] = w
y_condensed = pd.Series(y_condensed)
tol_connection = 0.9

# Graph
graph = nx.Graph()
for edge, w in y_condensed[lambda x: x > tol_connection].items():
    if len(edge) == 1:
        node = list(edge)[0]
        edge = (node, node)
    graph.add_edge(*edge, weight=abs(w))

# Plot network
nodes = list(graph.nodes())
weights = np.asarray(list(map(lambda x: x[-1]["weight"], graph.edges(data=True))))**2

pos = dict(zip(nodes, PCA(n_components=2, random_state=0).fit_transform(X_iris.loc[nodes])))
with plt.style.context("seaborn-white"):
    fig, ax = plt.subplots(figsize=(8,8))
    nx.draw_networkx_nodes(graph, pos=pos, node_color=c_iris[nodes], ax=ax, node_size=100, edgecolors="white", linewidths=1)
    nx.draw_networkx_edges(graph, pos=pos, width=weights, alpha=0.1618, ax=ax)

Now convert to a format for Datashader, and run hammer_bundle, and that's about all there is to do. (The first line is just a modification of a line from the OP, and for the edges I used int(n0.split("_")[1] which relies on the fact the number in the iris_# string is the same as the row number, which is a shortcut that doesn't generalize.)

nodes_py = [[name, a[0], a[1]] for name, a in zip(nodes, PCA(n_components=2, random_state=0).fit_transform(X_iris.loc[nodes]))]
ds_nodes = pd.DataFrame(nodes_py, columns=['name', 'x', 'y'])       

ds_edges_py = [[int(n0.split("_")[1]), int(n1.split("_")[1])] for (n0, n1) in graph.edges]
ds_edges = pd.DataFrame(ds_edges_py, columns=['source', 'target'])

hb = hammer_bundle(ds_nodes, ds_edges)

The hammer_bundle, hb is now a DataFrame with columns x and y and points that create the shapes of the curves. It is very large (283553 x 2) because it defines the curves by points rather than, say, Bezier curves, but this format is convenient for matplotlib. Different curves are separated by a row of NaNs (which also works well for matplotlib since when directly plotting, the NaNs will create breaks between the curves).

To plot the bundling curves directly from the DataFrame:

hb.plot(x="x", y="y", figsize=(9,9))

It can easily be combined, with say, the original analysis:

with plt.style.context("seaborn-white"):
    fig, ax = plt.subplots(figsize=(8,8))
    ax.plot(hb.x, hb.y, 'y', zorder=1, linewidth=3)
    nx.draw_networkx_nodes(graph, pos=pos, node_color=c_iris[nodes], ax=ax, node_size=50, edgecolors='white', linewidths=1)
    nx.draw_networkx_edges(graph, pos=pos, width=weights, alpha=0.1618, ax=ax)

Or plot every bundle in a different color (here, every 50th bundle for visibility):

hbnp = hb.to_numpy()
splits = (np.isnan(hbnp[:,0])).nonzero()[0]

start = 0
segments = []
for stop in splits:
    seg = hbnp[start:stop, :]
    segments.append(seg)
    start = stop

fig, ax = plt.subplots(figsize=(7,7))
for seg in segments[::50]:
    ax.plot(seg[:,0], seg[:,1])