def collect_res_bug(cls,
key_path=[],
nameFile=None,
allPrefix='res_',
folderName=None):
"""Extract results stored in (a) txt file(s) and group them according to
some key values (where key_path provides the path in the potentially
nested structure of the results to find the key(s))
Output:
a dictionary where key is the concatenation of the unique set of
keys found and value is the res is a list of all res matching this key
"""
listRes = cls.read_res_bug(nameFile, allPrefix, folderName)
res_keys = [
tuple([ut.extract_from_nested(res, k) for k in key_path])
for res in listRes
]
res_keys_unique = list(set(res_keys))
res = {
ut.concat2String(*k_u):
[listRes[n] for n, r in enumerate(res_keys) if r == k_u]
for k_u in res_keys_unique
}
return res
def collect_res(cls, key_path = [], nameFile = None, allPrefix = 'res_',
folderName = None, replace_func = None):
""" collect results and group them according to some key values
Arguments
---------
key_path: <list>
if not empty defines how to group results: provides a path in the
dict structure of the results to get a value used for the grouping
nameFile, allPrefix, folderName: cf. read_res()
Output:
-------
a dictionary where key is the concatenation of the unique set of
keys found and value is the res is a list of all res matching this key
"""
listRes, listNames = cls.read_res(nameFile, allPrefix, folderName,
returnName=True, replace_func=replace_func)
if(len(key_path) == 0):
res = {k:v for k,v in zip(listNames, listRes)}
else:
res_keys = [tuple([ut.extract_from_nested(res, k) for k in key_path])
for res in listRes]
res_keys_unique = list(set(res_keys))
res = {ut.concat2String(*k_u):[listRes[n] for n, r in enumerate(res_keys)
if r == k_u] for k_u in res_keys_unique}
return res
def _gen_frequencies(self, T, nb_freq=1, freq_type=None):
"""Generate (potentially randomized) frequencies based on 'freq_type'
'principal' or None om[l] = 2 * Pi * l /T
'CRAB' om[l] = 2 * Pi * l * (1 + eps[l]) /T with eps iid U[-0.5, 0.5]
'DCRAB' om[l] ~ U[0, w_max]
others
"""
Om_ref = 2 * np.pi / T
#dico_args = {'freq_type':freq_type}
args_rdm = freq_type.split("_")
rgen = self._rdm_gen #Use this rdamgen (provided or created at init of the factory)
if (args_rdm[0] in [None, 'principal']):
Om = (1 + np.arange(nb_freq)) * Om_ref
elif (args_rdm[0] == 'CRAB'):
rdv_method = 'uniform_-0.5_0.5'
rdvgen = rgen.gen_rdmnb_from_string(rdv_method, nb_freq)
Om = (1 + np.arange(nb_freq) + rdvgen) * Om_ref
elif (args_rdm[0] == 'DCRAB'):
if (len(args_rdm) > 1):
Nmax = int(args_rdm[1])
else:
Nmax = nb_freq
wmax = Nmax * Om_ref
rdv_method = ut.concat2String('uniform', 0, wmax)
Om = rgen.gen_rdmnb_from_string(rdv_method, nb_freq)
Om = np.sort(Om)
#dico_args['flag'] = 'DCRAB'
# Omega is also a param with some specific boundaries
elif (args_rdm[0] == 'CRAB_FREEOM'):
om = (1 + np.arange(nb_freq)) * Om_ref
om_bounds = [(0.5 + nb, 1.5 + nb) * Om_ref
for nb in np.arange(nb_freq)]
Om = (om, om_bounds)
else:
Om = rgen.gen_rdmnb_from_string(freq_type, nb_freq)
Om = np.sort(Om)
#dico_args['omegas'] = om
return Om
