Changing the Plot Order to Effectively Gray Out Certain Paths

[ryanpapetti]

Is there a current way to change the Z-order of the plot? I would like the plot below to have the green lines plot on top of the (almost) transparent ones.

Code:

import pandas as pd, numpy as np
raw_sample_data = pd.DataFrame({'source': {0: '0',
  1: '0',
  2: '0',
  3: '0',
  4: '0',
  5: '0',
  6: '0',
  7: '0',
  8: '1',
  9: '1',
  10: '1',
  11: '1',
  12: '1',
  13: '1',
  14: '1',
  15: '1',
  16: '2',
  17: '2',
  18: '2',
  19: '2',
  20: '2',
  21: '2',
  22: '2',
  23: '2',
  24: '3',
  25: '3',
  26: '3',
  27: '3',
  28: '3',
  29: '3',
  30: '3',
  31: '3',
  32: '4',
  33: '4',
  34: '4',
  35: '4',
  36: '4',
  37: '4',
  38: '4',
  39: '4',
  40: '5',
  41: '5',
  42: '5',
  43: '5',
  44: '5',
  45: '5',
  46: '5',
  47: '5',
  48: '6',
  49: '6',
  50: '6',
  51: '6',
  52: '6',
  53: '6',
  54: '6',
  55: '6'},
 'target': {0: '0',
  1: '1',
  2: '2',
  3: '3',
  4: '4',
  5: '5',
  6: '6',
  7: '7',
  8: '0',
  9: '1',
  10: '2',
  11: '3',
  12: '4',
  13: '5',
  14: '6',
  15: '7',
  16: '0',
  17: '1',
  18: '2',
  19: '3',
  20: '4',
  21: '5',
  22: '6',
  23: '7',
  24: '0',
  25: '1',
  26: '2',
  27: '3',
  28: '4',
  29: '5',
  30: '6',
  31: '7',
  32: '0',
  33: '1',
  34: '2',
  35: '3',
  36: '4',
  37: '5',
  38: '6',
  39: '7',
  40: '0',
  41: '1',
  42: '2',
  43: '3',
  44: '4',
  45: '5',
  46: '6',
  47: '7',
  48: '0',
  49: '1',
  50: '2',
  51: '3',
  52: '4',
  53: '5',
  54: '6',
  55: '7'},
 'value': {0: 0.28378378378378377,
  1: 0.20270270270270271,
  2: 0.013513513513513514,
  3: 0.013513513513513514,
  4: 0.47297297297297297,
  5: 0.013513513513513514,
  6: 0.0,
  7: 0.0,
  8: 0.05042016806722689,
  9: 0.10084033613445378,
  10: 0.37815126050420167,
  11: 0.44537815126050423,
  12: 0.008403361344537815,
  13: 0.008403361344537815,
  14: 0.0,
  15: 0.008403361344537815,
  16: 0.1,
  17: 0.058333333333333334,
  18: 0.016666666666666666,
  19: 0.008333333333333333,
  20: 0.0,
  21: 0.0,
  22: 0.7333333333333333,
  23: 0.08333333333333333,
  24: 0.020202020202020204,
  25: 0.09090909090909091,
  26: 0.6464646464646465,
  27: 0.010101010101010102,
  28: 0.050505050505050504,
  29: 0.0,
  30: 0.06060606060606061,
  31: 0.12121212121212122,
  32: 0.3779527559055118,
  33: 0.13385826771653545,
  34: 0.023622047244094488,
  35: 0.28346456692913385,
  36: 0.12598425196850394,
  37: 0.007874015748031496,
  38: 0.047244094488188976,
  39: 0.0,
  40: 0.013333333333333334,
  41: 0.08666666666666667,
  42: 0.12666666666666668,
  43: 0.0,
  44: 0.006666666666666667,
  45: 0.0,
  46: 0.36666666666666664,
  47: 0.4,
  48: 0.34285714285714286,
  49: 0.0,
  50: 0.0,
  51: 0.0,
  52: 0.0,
  53: 0.6285714285714286,
  54: 0.0,
  55: 0.02857142857142857}})


from floweaver import *
raw_sample_data = pd.DataFrame(dispo_stv_data_mapping['Worcester'][0],columns = ['source', 'target', 'value'])
raw_sample_data['source'] = raw_sample_data['source'].apply(str)
raw_sample_data['target'] = raw_sample_data['target'].apply(str)

# sample_data['type'] = 'customer'
sample_data = Dataset(raw_sample_data)
size = dict(width=570, height=300)

# partition_june = sample_data.partition('source')
partition_june = Partition.Simple('source', np.unique(raw_sample_data['source']))
# partition_july = sample_data.partition('target')
partition_july = Partition.Simple('target', np.unique(raw_sample_data['target']))

nodes = {
    'June 2 Clustering': ProcessGroup(sorted(list(set(raw_sample_data['source'])))),
    'July 15 Clustering': ProcessGroup(sorted(list(set(raw_sample_data['target']))))
}

ordering = [['June 2 Clustering'], ['July 15 Clustering']]

bundles = [
    Bundle('June 2 Clustering', 'July 15 Clustering'),
]

source_broken_out_partitions = Partition.Simple('process',sorted(list(set(raw_sample_data['source']))))
target_broken_out_partitions = Partition.Simple('process',sorted(list(set(raw_sample_data['target']))))

nodes['June 2 Clustering'].partition = source_broken_out_partitions
nodes['July 15 Clustering'].partition = target_broken_out_partitions

size_options = dict(width=800, height=640,
                    margins=dict(left=100, right=100))

#to make custom colorings
class MyScale(QuantitativeScale):
    def get_palette(self, link):
        # Choose colour scheme based on link type (here, Employment Job)
        name = 'Greens_9' if link.source == 'June 2 Clustering^1' else 'Greys_9'
#         name = 'Greens_9' if link.source == 'June 2 Clustering^1' else 'Whites_9'
#         print(link);input();print(link.target,link.source,link.type);input()
        return self.lookup_palette_name(name)

    def get_color(self, link, value):
        palette = self.get_palette(link)
        if link.source == 'June 2 Clustering^1':
            return palette(100)
#         IS IT POSSIBLE TO SET VALUE TO FALSE OR NONE?
        return palette(0*value)


sdd = SankeyDefinition(nodes, bundles, ordering, flow_partition = partition_june)
my_scale = MyScale('value')
weave(sdd, raw_sample_data,link_color = my_scale).to_widget(**size_options) #NEARLY PERFECT PLOT BUT DOES NOT HAVE PROPER Z ORDER

Currently, I am subclassing QuantitativeScale as in the docs to only color based on the source. Perhaps it's possible to turn off the links somewhere as well?