-
Notifications
You must be signed in to change notification settings - Fork 1
/
roc_app.py
238 lines (206 loc) · 6.94 KB
/
roc_app.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
"""
Copyright (C) 2023 - PRESENT Zhengyu Peng
E-mail: [email protected]
Website: https://zpeng.me
` `
-:. -#:
-//:. -###:
-////:. -#####:
-/:.://:. -###++##:
.. `://:- -###+. :##:
`:/+####+. :##:
.::::::::/+###. :##:
.////-----+##: `:###:
`-//:. :##: `:###/.
`-//:. :##:`:###/.
`-//:+######/.
`-/+####/.
`+##+.
:##:
:##:
:##:
:##:
:##:
.+:
"""
import dash
from dash.dependencies import Input, Output, State
from dash.exceptions import PreventUpdate
import dash_bootstrap_components as dbc
import numpy as np
import plotly.io as pio
from roc.tools import roc_snr, roc_pd
# from flaskwebgui import FlaskUI
from layout.layout import get_app_layout, sidebar_pdpfa, sidebar_gain
app = dash.Dash(
__name__,
suppress_callback_exceptions=True,
meta_tags=[{"name": "viewport", "content": "width=device-width,initial-scale=1"}],
)
app.scripts.config.serve_locally = True
app.css.config.serve_locally = True
app.title = "ROC"
app.layout = get_app_layout
server = app.server
@app.callback(
[Output("sidebar", "children"), Output("hidden", "children")],
[Input("card-tabs", "active_tab")],
)
def tab_content(active_tab):
if active_tab == "tab-1":
return [sidebar_pdpfa, sidebar_gain]
elif active_tab == "tab-2":
return [sidebar_gain, sidebar_pdpfa]
@app.callback(
output={
"fig": Output("scatter", "figure", allow_duplicate=True),
},
inputs={
"n": Input("pdpfa-channels", "value"),
"snr": Input("pdpfa-snr", "value"),
"model": Input("pdpfa-integration", "value"),
},
state={
"min_n": State("pdpfa-channels", "min"),
"max_n": State("pdpfa-channels", "max"),
"min_snr": State("pdpfa-snr", "min"),
"max_snr": State("pdpfa-snr", "max"),
},
prevent_initial_call=True
)
def pdpfa_plot(n, snr, model, min_n, max_n, min_snr, max_snr):
"""
Generate a plot for integration gain based on probability of detection (Pd),
probability of false alarm (Pfa), number of channels (n), and a list of models.
Parameters:
- pd (float): Probability of detection.
- pfa (float): Probability of false alarm.
- n (int): Number of channels.
- model (list): List of models.
- min_pd (float): Minimum value for Pd.
- max_pd (float): Maximum value for Pd.
- min_pfa (float): Minimum value for Pfa.
- max_pfa (float): Maximum value for Pfa.
Raises:
- PreventUpdate: If pd is None, pd is outside the range [min_pd, max_pd],
pfa is None, or pfa is outside the range [min_pfa, max_pfa].
Returns:
dict: A dictionary containing the plot data and layout, as well as minsnr_container information.
- fig (dict): Plotly figure data and layout.
- minsnr_container (list): List of FormText containing minimum SNR information for each model.
"""
if n is None:
raise PreventUpdate
if n < min_n or n > max_n:
raise PreventUpdate
if snr is None:
raise PreventUpdate
if snr < min_snr or snr > max_snr:
raise PreventUpdate
pfa = np.logspace(-10, 0, 1000)
pfa = pfa[1:]
pd = roc_pd(pfa, snr, n, model)
fig_data = [
{
"mode": "lines",
"type": "scatter",
"x": np.log10(pfa),
"y": pd,
"name": model,
}
]
return {
"fig": {
"data": fig_data,
"layout": {
"template": pio.templates["plotly"],
"uirevision": "no_change",
"xaxis": {"title": "Probability of false alarm (Pfa)"},
"yaxis": {"title": "Probability of detection (Pd)"},
},
},
}
@app.callback(
output={
"fig": Output("scatter", "figure", allow_duplicate=True),
"minsnr_container": Output("minsnr-container", "children"),
},
inputs={
"pd": Input("pd", "value"),
"pfa": Input("pfa", "value"),
"n": Input("channels", "value"),
"model": Input("integration", "value"),
},
state={
"min_pd": State("pd", "min"),
"max_pd": State("pd", "max"),
"min_pfa": State("pfa", "min"),
"max_pfa": State("pfa", "max"),
},
prevent_initial_call=True
)
def gain_plot(pd, pfa, n, model, min_pd, max_pd, min_pfa, max_pfa):
"""
Generate a plot for integration gain based on probability of detection (Pd),
probability of false alarm (Pfa), number of channels (n), and a list of models.
Parameters:
- pd (float): Probability of detection.
- pfa (float): Probability of false alarm.
- n (int): Number of channels.
- model (list): List of models.
- min_pd (float): Minimum value for Pd.
- max_pd (float): Maximum value for Pd.
- min_pfa (float): Minimum value for Pfa.
- max_pfa (float): Maximum value for Pfa.
Raises:
- PreventUpdate: If pd is None, pd is outside the range [min_pd, max_pd],
pfa is None, or pfa is outside the range [min_pfa, max_pfa].
Returns:
dict: A dictionary containing the plot data and layout, as well as minsnr_container information.
- fig (dict): Plotly figure data and layout.
- minsnr_container (list): List of FormText containing minimum SNR information for each model.
"""
if pd is None:
raise PreventUpdate
if pd < min_pd or pd > max_pd:
raise PreventUpdate
if pfa is None:
raise PreventUpdate
if pfa < min_pfa or pfa > max_pfa:
raise PreventUpdate
n_array = np.arange(1, n + 1)
nci_gain = np.zeros((len(model), n), dtype=np.float64)
fig_data = []
minsnr_container = []
for m_idx, mod in enumerate(model):
minsnr = roc_snr(pfa, pd, 1, mod)
minsnr_container.append(
dbc.FormText(mod + ": " + str(round(minsnr, 3)) + " dB")
)
for idx in range(1, n + 1):
nci_gain[m_idx, idx - 1] = minsnr - roc_snr(pfa, pd, n_array[idx - 1], mod)
fig_data.append(
{
"mode": "lines",
"type": "scatter",
"x": n_array,
"y": nci_gain[m_idx, :],
"name": mod,
}
)
return {
"fig": {
"data": fig_data,
"layout": {
"template": pio.templates["plotly"],
"uirevision": "no_change",
"title": "Pd = " + str(pd) + ", Pfa = " + str(pfa),
"xaxis": {"title": "Number of Channels"},
"yaxis": {"title": "Integration Gain (dB)"},
},
},
"minsnr_container": minsnr_container,
}
if __name__ == "__main__":
app.run_server(debug=True, threaded=True, processes=1, host="0.0.0.0")
# FlaskUI(app=server, server="flask", port=61134, profile_dir="roc_app").run()