python-RAT/ratapi/examples/extras/two_contrast_example.py at acd43ae911a5e0dbc0774d686acce6dc50df1c1c · StephenNneji/python-RAT · GitHub

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
"""The example project with two contrasts from the user guide."""

import numpy as np

import ratapi as RAT


def two_contrast_example():
    """Generate the two-contrast example project from the user guide."""
    problem = RAT.Project(name="DSPC monolayers")
    parameters = [
        RAT.models.Parameter(name="Tails Thickness", min=10, value=20, max=30, fit=True),
        RAT.models.Parameter(name="Heads Thickness", min=3, value=11, max=16, fit=True),
        RAT.models.Parameter(name="Tails Roughness", min=2, value=5, max=9, fit=True),
        RAT.models.Parameter(name="Heads Roughness", min=2, value=5, max=9, fit=True),
        RAT.models.Parameter(name="Deuterated Tails SLD", min=4e-6, value=6e-6, max=2e-5, fit=True),
        RAT.models.Parameter(name="Hydrogenated Tails SLD", min=-0.6e-6, value=-0.4e-6, max=0, fit=True),
        RAT.models.Parameter(name="Deuterated Heads SLD", min=1e-6, value=3e-6, max=8e-6, fit=True),
        RAT.models.Parameter(name="Hydrogenated Heads SLD", min=0.1e-6, value=1.4e-6, max=3e-6, fit=True),
        RAT.models.Parameter(name="Heads Hydration", min=0, value=0.3, max=0.5, fit=True),
    ]

    problem.parameters.extend(parameters)
    H_Heads = RAT.models.Layer(
        name="Hydrogenated Heads",
        thickness="Heads Thickness",
        SLD="Hydrogenated Heads SLD",
        roughness="Heads Roughness",
        hydration="Heads Hydration",
        hydrate_with="bulk out",
    )

    D_Heads = RAT.models.Layer(
        name="Deuterated Heads",
        thickness="Heads Thickness",
        SLD="Deuterated Heads SLD",
        roughness="Heads Roughness",
        hydration="Heads Hydration",
        hydrate_with="bulk out",
    )

    D_Tails = RAT.models.Layer(
        name="Deuterated Tails", thickness="Tails Thickness", SLD="Deuterated Tails SLD", roughness="Tails Roughness"
    )

    H_Tails = RAT.models.Layer(
        name="Hydrogenated Tails",
        thickness="Tails Thickness",
        SLD="Hydrogenated Tails SLD",
        roughness="Tails Roughness",
    )

    problem.layers.extend([H_Heads, D_Heads, H_Tails, D_Tails])

    problem.background_parameters.set_fields(0, name="Backs Value ACMW")
    problem.background_parameters.set_fields(0, value=5.5e-6)
    problem.background_parameters.append(name="Backs Value D2O", min=1e-8, value=2.8e-6, max=1e-5)

    problem.backgrounds.append(name="Background D2O", type="constant", value_1="Backs Value D2O")
    problem.backgrounds.set_fields(0, name="Background ACMW", value_1="Backs Value ACMW")

    problem.bulk_out.append(name="SLD ACMW", min=-0.6e-6, value=-0.56e-6, max=-0.3e-6, fit=True)

    d13acmw20 = np.array(
        [
            [5.1793e-02, 1.8087e-05, 7.9683e-07],
            [5.4383e-02, 1.4582e-05, 6.3691e-07],
            [5.7102e-02, 1.3621e-05, 5.5407e-07],
            [5.9957e-02, 1.2409e-05, 4.8014e-07],
            [6.2955e-02, 1.0992e-05, 4.1824e-07],
            [6.6103e-02, 1.0619e-05, 3.7541e-07],
            [6.9408e-02, 1.0162e-05, 3.4945e-07],
            [7.2878e-02, 1.0716e-05, 3.3763e-07],
            [7.6522e-02, 8.3468e-06, 2.8040e-07],
            [8.0348e-02, 7.5250e-06, 2.5702e-07],
            [8.4365e-02, 6.4395e-06, 2.2528e-07],
            [8.8584e-02, 6.0544e-06, 2.0545e-07],
            [9.3013e-02, 5.9517e-06, 1.9417e-07],
            [9.7664e-02, 5.6433e-06, 1.8108e-07],
            [1.0255e-01, 4.8172e-06, 1.6076e-07],
            [1.0767e-01, 4.8066e-06, 1.5520e-07],
            [1.1306e-01, 4.1559e-06, 1.4115e-07],
            [1.1871e-01, 4.1418e-06, 1.3926e-07],
            [1.2465e-01, 3.4580e-06, 1.2273e-07],
            [1.3088e-01, 3.2376e-06, 1.1791e-07],
            [1.3742e-01, 3.0976e-06, 1.1948e-07],
            [1.4429e-01, 2.7478e-06, 1.1518e-07],
            [1.5151e-01, 2.5492e-06, 1.1573e-07],
            [1.5908e-01, 2.7500e-06, 1.2812e-07],
            [1.6704e-01, 2.1813e-06, 2.0450e-07],
            [1.7539e-01, 1.8609e-06, 5.4900e-07],
            [1.8416e-01, 1.9405e-06, 1.5660e-07],
            [1.9337e-01, 1.7421e-06, 2.3280e-07],
            [2.0304e-01, 1.8050e-06, 3.9820e-07],
            [2.1319e-01, 1.5801e-06, 1.4110e-07],
            [2.2385e-01, 1.6724e-06, 7.7900e-08],
            [2.3504e-01, 1.4150e-06, 4.0820e-07],
            [2.4679e-01, 1.4340e-06, 5.3180e-07],
            [2.5913e-01, 1.3102e-06, 2.6000e-07],
            [2.7209e-01, 1.3702e-06, 2.8540e-07],
            [2.8569e-01, 1.2468e-06, 2.3230e-07],
            [2.9998e-01, 1.2956e-06, 5.3240e-07],
            [3.1497e-01, 1.2947e-06, 3.9940e-07],
            [3.3072e-01, 1.2488e-06, 2.1390e-07],
            [3.4726e-01, 1.4620e-06, 3.3640e-07],
            [3.6462e-01, 1.3056e-06, 2.1600e-07],
            [3.8285e-01, 1.4553e-06, 2.3170e-07],
            [4.0200e-01, 1.1579e-06, 2.6740e-07],
            [4.2210e-01, 1.1753e-06, 3.0940e-07],
            [4.4320e-01, 1.0066e-06, 5.2040e-07],
            [4.6536e-01, 1.1043e-06, 3.1870e-07],
            [4.8863e-01, 1.2969e-06, 4.1670e-07],
            [5.1306e-01, 1.2495e-06, 2.0890e-07],
            [5.3871e-01, 1.3898e-06, 4.7560e-07],
            [5.6565e-01, 1.1225e-06, 3.0470e-07],
            [5.8877e-01, 8.3346e-07, 3.8944e-07],
        ]
    )

    d70d2o20 = np.array(
        [
            [5.1793e-02, 1.4943e-04, 3.2517e-06],
            [5.4383e-02, 1.1882e-04, 2.5846e-06],
            [5.7102e-02, 9.2164e-05, 2.0507e-06],
            [5.9957e-02, 7.7813e-05, 1.7070e-06],
            [6.2955e-02, 6.1143e-05, 1.3983e-06],
            [6.6103e-02, 4.8033e-05, 1.1343e-06],
            [6.9408e-02, 4.1379e-05, 1.0006e-06],
            [7.2878e-02, 3.5090e-05, 8.6919e-07],
            [7.6522e-02, 3.0350e-05, 7.5890e-07],
            [8.0348e-02, 2.4115e-05, 6.5226e-07],
            [8.4365e-02, 2.0755e-05, 5.7445e-07],
            [8.8584e-02, 1.7500e-05, 4.9617e-07],
            [9.3013e-02, 1.5011e-05, 4.3754e-07],
            [9.7664e-02, 1.3632e-05, 3.9907e-07],
            [1.0255e-01, 1.2997e-05, 3.7469e-07],
            [1.0767e-01, 1.1656e-05, 3.4282e-07],
            [1.1306e-01, 1.0820e-05, 3.2299e-07],
            [1.1871e-01, 9.5831e-06, 3.0088e-07],
            [1.2465e-01, 8.9996e-06, 2.8102e-07],
            [1.3088e-01, 8.6498e-06, 2.7363e-07],
            [1.3742e-01, 7.9412e-06, 2.7158e-07],
            [1.4429e-01, 7.4042e-06, 2.6829e-07],
            [1.5151e-01, 6.8321e-06, 2.6877e-07],
            [1.5908e-01, 5.6809e-06, 2.6232e-07],
            [1.6704e-01, 5.6646e-06, 2.8607e-07],
            [1.7539e-01, 4.7762e-06, 2.7767e-07],
            [1.8416e-01, 4.1971e-06, 2.7353e-07],
            [1.9337e-01, 4.1815e-06, 2.9140e-07],
            [2.0304e-01, 3.2725e-06, 2.3160e-07],
            [2.1319e-01, 2.3244e-06, 4.2270e-07],
            [2.2385e-01, 2.2892e-06, 1.4970e-07],
            [2.3504e-01, 1.9198e-06, 8.1460e-07],
            [2.4679e-01, 1.4828e-06, 1.0840e-07],
            [2.5913e-01, 1.1450e-06, 4.7440e-07],
            [2.7209e-01, 1.3047e-06, 1.8840e-07],
            [2.8569e-01, 9.6081e-07, 3.9385e-07],
            [2.9998e-01, 8.2280e-07, 2.3955e-07],
            [3.1497e-01, 6.3219e-07, 3.5324e-07],
            [3.3072e-01, 6.1254e-07, 5.0846e-07],
            [3.4726e-01, 7.4092e-07, 2.2312e-07],
            [3.6462e-01, 6.3584e-07, 4.2866e-07],
            [3.8285e-01, 7.7287e-07, 2.9493e-07],
            [4.0200e-01, 9.4637e-07, 2.3347e-07],
            [4.2210e-01, 7.0576e-07, 3.3494e-07],
            [4.4320e-01, 7.8969e-07, 2.3371e-07],
            [4.6536e-01, 5.8374e-07, 3.2624e-07],
            [4.8863e-01, 5.6711e-07, 6.0419e-07],
            [5.1306e-01, 4.7782e-07, 3.4822e-07],
            [5.3871e-01, 6.3813e-07, 4.3279e-07],
            [5.6565e-01, 4.6186e-07, 1.8863e-07],
            [5.8877e-01, 5.6129e-07, 4.3960e-07],
        ]
    )

    problem.data.append(name="H-tail / D-head / ACMW", data=d13acmw20)
    problem.data.append(name="D-tail / H-head / D2O", data=d70d2o20)

    problem.contrasts.append(
        name="D-tail/H-Head/D2O",
        background="Background D2O",
        resolution="Resolution 1",
        scalefactor="Scalefactor 1",
        bulk_out="SLD D2O",
        bulk_in="SLD Air",
        data="D-tail / H-head / D2O",
    )

    problem.contrasts.append(
        name="H-tail/D-Head/ACMW",
        background="Background ACMW",
        resolution="Resolution 1",
        scalefactor="Scalefactor 1",
        bulk_out="SLD ACMW",
        bulk_in="SLD Air",
        data="D-tail / H-head / D2O",
    )

    problem.contrasts.set_fields(0, model=["Deuterated Tails", "Hydrogenated Heads"])
    problem.contrasts.set_fields(1, model=["Hydrogenated Tails", "Deuterated Heads"])

    problem.background_parameters.set_fields(0, fit=True)
    problem.background_parameters.set_fields(1, fit=True)
    problem.scalefactors.set_fields(0, fit=True)
    problem.bulk_out.set_fields(0, fit=True)

    return problem


if __name__ == "__main__":
    problem = two_contrast_example()
    controls = RAT.Controls()
    problem, results = RAT.run(problem, controls)

    RAT.plotting.plot_ref_sld(problem, results, True)