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
|
'''
'''
from __future__ import annotations
import typing
import yaml
STRUCTURE={
'url':str,
'token':str,
'owner':str,
'repo':str,
'feeds':list,
'label':str
}
STRUCTURE_DICT_LIST={
'feeds': {
'url':str,
'name':str,
'assign':str,
'exclude':list,
'include':list,
}
}
class ConfigError(Exception):
pass
class Config:
'''
Imports and stores curvegenerator parameters
Example:
```yaml
# test.yaml
---
testval: 4
```
```python
conf = Config("test.yaml")
print(conf.testval)
```
'''
def __init__(self, _file):
'''
Constructor
Args:
_file (str): Filename to yaml configfile
'''
if _file is None:
return
with open(_file, 'r') as f:
self.config = yaml.load(f.read(), Loader=yaml.FullLoader)
self._validate()
def __iter__(self):
self.n = 0
pass
def __next__(self):
pass
def __getitem__(self, _key):
self.config[_key]
@staticmethod
def _validate_dict(_dict, _spec, _context=''):
for e in _spec:
if e not in _dict:
if _spec[e] is not list:
raise ConfigError(f'{_context}Key {e} is not set.')
else:
pass
elif type(_dict[e]) is not _spec[e]:
raise ConfigError(f'{_context}Key {e} is {type(_dict[e])}. Should be {_spec[e]}')
def _validate(self):
Config._validate_dict(self.config, STRUCTURE)
for lst in STRUCTURE_DICT_LIST:
for e in self.config[lst]:
Config._validate_dict(e, STRUCTURE_DICT_LIST[lst], 'feeds: ')
def load(self, _dict):
self.config = _dict
def __getattr__(self, _attr) -> str | Config | None:
if _attr not in self.config:
return None
if isinstance(self.config[_attr], dict):
ret = Config(None)
ret.load(self.config[_attr])
return ret
return self.config[_attr]
def __str__(self):
return yaml.dump(self.config)
|