File size: 8,027 Bytes
74e10df
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
#!/usr/bin/python
"""
Parses RASPA output, returning a mutable Python object.
String output is organized in Python dictionary object.

##############        COMMON DATA       ##################
Below shows how to retrieve some common values after running RASPA

info = RASPA.run(...)

1) Average loading absolute

info["Number of molecules"][MOLECULE_NAME][Unit_Name][0]

# specific example with Nitrogen molecule

info["Number of molecules:"]["N2"][insert below units][0]

5 units are specified from RASPA output
YOU MUST INSERT ENTIRE STRIG WITHIN BRACKETS

["Average loading absolute [molecules/unit cell]"]
["Average loading absolute [cm^3 (STP)/gr framework]"]
["Average loading absolute [mol/kg framework]"]
["Average loading absolute [milligram/gram framework]"]
["Average loading absolute [cm^3 (STP)/cm^3 framework]"]


2) Average loading excess

info["Number of molecules"][MOLECULE_NAME][Unit_Name][0]

# specific example with Nitrogen molecule

info["Number of molecules"]["N2"][insert below units][0]

5 units are specified from RASPA output
YOU MUST INSERT ENTIRE STRIG WITHIN BRACKETS

["Average loading excess [molecules/unit cell]"]
["Average loading excess [cm^3 (STP)/gr framework]"]
["Average loading excess [mol/kg framework]"]
["Average loading excess [milligram/gram framework]"]
["Average loading excess [cm^3 (STP)/cm^3 framework]"]


3) Helium Void Fraction

   info["Average Widom Rosenbluth factor"]["Widom"][0]

4) Henry coefficient

   info["Average Henry coefficient:"]["Henry"][0]

5) Heat of desorption values

   info["Heat of desorption"]["[K]"][0]
               "              ["[KJ/MOL]"][0]

   info["Heat of desorption (Host-Adsorbate contribution)"]["[K]"][0]
               "                                       ["[KJ/MOL]"][0]

   info["Heat of desorption (Adsorbate-Adsorbate contribution)"]["[K]"][0]
               "                                            ["[KJ/MOL]"][0]

6) Geometric Surface Area:

   info["Average Surface Area"]["[m^2/g]"][0]
   info["Average Surface Area"]["[m^2/cm^3]"][0]

"""
import re


def parse(raspa_output):
    """Specific parsing of the output file.

    Args:
        raspa_output: A string representing unparsed RASPA output
    Returns:
        A data structure generated from the RASPA file
    """
    # Reads the string into a newline-separated list, skipping useless lines
    data = [row.strip() for row in raspa_output.splitlines() if row and
            all(d not in row for d in ["-----", "+++++"])]

    # Generally, categories in the output are delimited by equal signs
    delimiters = [i for i, row in enumerate(data) if "=====" in row
                  and "Exclusion constraints energy" not in data[i - 1]]

    # Append a row for "absolute adsorption:" and "excess adsorption:"
    # These values are separated into two rows
    abs_adsorp_rows = [i for i, row in enumerate(data)
                       if "absolute adsorption:" in row]
    for row in abs_adsorp_rows:
        data[row] += "  " + data[row + 1]
        data[row + 2] += data[row + 3]
        data[row + 1], data[row + 3] = " ", " "

    # Use the delimiters to make a high-level dict. Title is row before
    # delimiter, and content is every row after delimiter, up to the next title
    info = {data[n - 1].strip(":"): data[n + 1: delimiters[i + 1] - 1]
            for i, n in enumerate(delimiters[:-1])}

    # Let's PARSE!
    for key, values in info.items():
        d, note_index = {}, 1
        for item in values:
            # Takes care of all "Blocks[ #]", skipping hard-to-parse parts
            if ("Block" in item and "Box-lengths" not in key
                    and "Van der Waals:" not in item):
                blocks = _clean(item.split())
                d["".join(blocks[:2])] = blocks[2:]

            # Most of the average data values are parsed in this section
            elif (any(s in item for s in ["Average     ", "Surface area:"])
                  and "desorption" not in key):
                average_data = _clean(item.split())
                # Average values organized by its unit, many patterns here
                if len(average_data) == 8:
                    del average_data[2:4]
                    d[" ".join(average_data[4:6])] = average_data[1:4]
                elif len(average_data) == 5:
                    d[average_data[-1]] = average_data[1:4]
                elif "Surface" in average_data[0]:
                    d[average_data[-1]] = average_data[2:5]
                # This is the common case
                else:
                    del average_data[2]
                    d[average_data[-1]] = average_data[1:4]

            # Average box-lengths has its own pattern
            elif "Box-lengths" in key:
                box_lengths = _clean(item.split())
                i = 3 if "angle" in item else 2
                d[" ".join(box_lengths[:i])] = box_lengths[i:]

            # "Heat of Desorption" section
            elif "desorption" in key:
                if "Note" in item:
                    notes = re.split("[:\s]{2,}", item)
                    d["%s %d" % (notes[0], note_index)] = notes[1]
                    note_index += 1
                else:
                    heat_desorp = _clean(item.split())
                    # One line has "Average" in front, force it to be normal
                    if "Average" in item:
                        del heat_desorp[0]
                    d[heat_desorp[-1]] = heat_desorp[0:3]

            # Parts where Van der Waals are included
            elif ("Host-" in key or "-Cation" in key or
                  "Adsorbate-Adsorbate" in key) and "desorption" not in key:
                van_der = item.split()
                # First Column
                if "Block" in van_der[0]:
                    sub_data = [_clean(s.split(":"))
                                for s in re.split("\s{2,}", item)[1:]]
                    sub_dict = {s[0]: s[1] for s in sub_data[:2]}
                    d["".join(van_der[:2])] = [float(van_der[2]), sub_dict]
                # Average for each columns
                elif "Average" in item:
                    avg = _clean(re.split("\s{2,}", item))
                    vdw, coulomb = [_clean(s.split(": ")) for s in avg[2:4]]
                    d[avg[0]] = avg[1]
                    d["Average %s" % vdw[0]] = vdw[1]
                    d["Average %s" % coulomb[0]] = coulomb[1]
                else:
                    d["standard deviation"] = _clean(van_der)

            # IMPORTANT STUFF
            elif "Number of molecules" in key:
                adsorb_data = _clean(item.rsplit(" ", 12))
                if "Component" in item:
                    gas_name = adsorb_data[2].strip("[]")
                    d[gas_name] = {}
                else:
                    d[gas_name][adsorb_data[0]] = adsorb_data[1:]

            # Henry and Widom
            elif "Average Widom" in item:
                d["Widom"] = _clean(item.rsplit(" ", 5))[1:]

            elif "Average Henry" in item:
                d["Henry"] = _clean(item.rsplit(" ", 5))[1:]

            # Ignore these
            elif any(s in item for s in ["=====", "Starting simulation",
                     "Finishing simulation"]):
                continue

            # Other strings
            else:
                parsed_data = _clean(re.split("[()[\]:,\t]", item))
                d[parsed_data[0]] = parsed_data[1:]
        # Putting subdictionary back into main object
        info[key] = d

    return info


def _clean(split_list):
    """Strips and attempts to convert a list of strings to floats."""

    def try_float(s):
        try:
            return float(s)
        except ValueError:
            return s

    return [try_float(s.strip()) for s in split_list if s]


if __name__ == "__main__":
    import sys
    import json
    with open(sys.argv[-1]) as in_file:
        data = in_file.read()
    print(json.dumps(parse(data), indent=4, sort_keys=True,
                     separators=[",", ":"]))