-
Notifications
You must be signed in to change notification settings - Fork 0
/
node_viz.py
248 lines (213 loc) · 8.07 KB
/
node_viz.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import streamlit as st
def filter_df(df, num_sources=None, num_targets=None):
"""
A function to filter the data frame by top n sources and targets
If num_sources or num_targets args are not supplied, they will not be filtered
"""
if num_targets:
top_targets = df.sum().sort_values(ascending=False)
df = df[top_targets[:num_targets].index]
if num_sources:
top_sources = df.sum(axis=1).sort_values(ascending=False)[:num_sources]
df = df.loc[top_sources.index]
return df
def create_sankey_df(df, min_val=0):
"""
Create the human-readable form of the Sankey chart data from an input data frame
Data can be filtered by a threshold minimum value
| Source | Source Value | Target | Target Value |
| A | 5 | i | 3 |
| A | 5 | j | 2 |
| B | 7 | i | 1 |
| B | 7 | k | 4 |
"""
sources = []
source_vals = []
targets = []
target_vals = []
for source_name in df.index:
row = df.loc[source_name]
sources += [source_name] * sum(row.values > min_val)
source_vals += [row[row.values > min_val].sum()] * sum(row.values > min_val)
targets += list(row[row > min_val].index)
target_vals += list(row[row > min_val].values)
sankey_df = pd.DataFrame({
'source': sources,
'source_value': source_vals,
'target': targets,
'target_value': target_vals
})
return sankey_df
def create_sankey_dict(sankey_df):
"""
Plotly requires that each source and target be converted to a numerical index
This index also points to an entry in the labels file
As a convention that I think will be useful, indices for a target build off of
the last value in the preceding column's indices
"""
source_nodes = sorted(sankey_df.source.unique())
source_node_dict = {source_nodes[i]: i for i in range(len(source_nodes))}
target_nodes = sorted(sankey_df.target.unique())
target_node_dict = {target_nodes[i]: i + max(source_node_dict.values()) + 1 for i in range(len(target_nodes))}
source_indices = list(sankey_df.source.map(source_node_dict).values)
source_values = list(sankey_df.source_value)
target_indices = list(sankey_df.target.map(target_node_dict).values)
target_values = list(sankey_df.target_value)
sankey_dict = {
'source_labels': source_nodes,
'source': source_indices,
'source_values': source_values,
'target_labels': target_nodes,
'target': target_indices,
'target_values': target_values
}
return sankey_dict
def plot_sankey(sankey_dict, title):
"""Plot a Sankey diagram. By default, line height is given by the target values"""
# Figure configuration
layout = go.Layout(
paper_bgcolor = 'rgb(228, 218, 204)',
plot_bgcolor = 'rgb(228, 218, 204)'
)
fig = go.Figure(data=[go.Sankey(
node = dict(
pad = 15,
thickness = 20,
line = dict(color = "black", width = 0.5),
label = sankey_dict['source_labels'] + sankey_dict['target_labels'],
color = "black"
),
link = dict(
source = sankey_dict['source'],
target = sankey_dict['target'],
value = sankey_dict['target_values']
))], layout=layout)
fig.update_layout(title_text=title, font_size=10)
return fig
@st.cache(persist=True)
def load_data():
investor = pd.read_excel(
"./data/Equity investor SUP matrix.xlsx",
engine="openpyxl",
skiprows=3,
usecols="B, E:GG",
)
investor = investor.rename(columns={investor.columns[0]: "Ultimate Investor"})
# drop last row because it is a table summary
investor = investor[:-1]
investor = investor.set_index('Ultimate Investor')
financer = pd.read_excel(
"./data/Financing SUP matrix.xlsx",
engine="openpyxl",
skiprows=4,
usecols="A:AV",
)
# drop last row because it is null
financer = financer[:-1]
financer = financer.set_index('Bank')
producer = pd.read_excel(
"./data/MFA matrix.xlsx",
sheet_name="Conversion",
engine="openpyxl",
skiprows=1,
usecols="C:FY",
).dropna()
producer = producer.groupby('Producer').sum()
waste = pd.read_excel(
"./data/MFA matrix.xlsx",
sheet_name="Waste",
engine="openpyxl",
skiprows=1,
usecols="B, D:FY",
).dropna()
waste = waste.groupby('Country').sum()
return investor, financer, producer, waste
st.header("Global Plastic Polluters Index")
investor, financer, producer, waste = load_data()
plot_config = {'displayModeBar': False}
st.markdown("""
PPI Node 1-2: Financer OR Investor to Producer
""")
financing_type = st.selectbox(
'Financing type',
['Investor', 'Financer']
)
if financing_type == 'Financer':
n_money_sources = st.slider(
'Number of top funding sources to visualize',
1, len(financer), 10
)
n_producer_targets = st.slider(
'Number of top producers to visualize',
1, len(financer.columns), 10
)
minimum_financing_value = st.slider(
'Threshold for minimum funding value',
0, int(financer.max().max()), 500
)
financer_df = filter_df(financer, num_sources=n_money_sources, num_targets=n_producer_targets)
financer_sankey_df = create_sankey_df(financer_df, min_val=minimum_financing_value)
financer_sankey_dict = create_sankey_dict(financer_sankey_df)
financer_plot = plot_sankey(financer_sankey_dict, 'Financer to Producer')
st.plotly_chart(financer_plot, use_container_width=True, config=plot_config)
if financing_type == 'Investor':
n_money_sources = st.slider(
'Number of top funding sources to visualize',
1, len(investor), 10
)
n_producer_targets = st.slider(
'Number of top producers to visualize',
1, len(investor.columns), 10
)
minimum_financing_value = st.slider(
'Threshold for minimum funding value',
0, int(investor.max().max()), 100
)
investor_df = filter_df(investor, num_sources=n_money_sources, num_targets=n_producer_targets)
investor_sankey_df = create_sankey_df(investor_df, min_val=minimum_financing_value)
investor_sankey_dict = create_sankey_dict(investor_sankey_df)
investor_plot = plot_sankey(investor_sankey_dict, 'Investor to Producer')
st.plotly_chart(investor_plot, use_container_width=True, config=plot_config)
st.markdown("""
PPI Node 2-3: Producer to Country of Production
""")
n_producer_sources = st.slider(
'Number of top producers to visualize',
1, len(producer), 10
)
n_country_targets = st.slider(
'Number of top countries to visualize',
1, len(producer.columns), 10
)
minimum_production_value = st.slider(
'Threshold for minimum production volume',
0, int(producer.max().max()), 1500
)
producer_df = filter_df(producer, num_sources=n_producer_sources, num_targets=n_country_targets)
producer_sankey_df = create_sankey_df(producer_df, min_val=minimum_production_value)
producer_sankey_dict = create_sankey_dict(producer_sankey_df)
producer_plot = plot_sankey(producer_sankey_dict, 'Producer to Country of Production')
st.plotly_chart(producer_plot, use_container_width=True, config=plot_config)
st.markdown("""
PPI Node 3-4: Country of Production to Country of Impact
""")
n_country_sources = st.slider(
'Number of top producers to visualize',
1, len(waste), 10
)
n_waste_targets = st.slider(
'Number of top destination countries to visualize',
1, len(waste.columns), 10
)
minimum_waste_value = st.slider(
'Threshold for minimum waste volume',
0, int(waste.max().max()), 1000
)
waste_df = filter_df(waste, num_sources=n_country_sources, num_targets=n_waste_targets)
waste_sankey_df = create_sankey_df(waste_df, min_val=minimum_waste_value)
waste_sankey_dict = create_sankey_dict(waste_sankey_df)
waste_plot = plot_sankey(waste_sankey_dict, 'Country of Production to Country of Impact')
st.plotly_chart(waste_plot, use_container_width=True, config=plot_config)