-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathMR.py
228 lines (194 loc) · 8.8 KB
/
MR.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
from sddsdata import sddsdata
import numpy as np
import scipy.io as sio
import scipy.optimize as op
import os
import shutil
import gzip
import time
import glob
import pickle
from zip_mat import save_zip
import timestamp_helpers as th
class MountainRange(object):
def __init__(self, complete_path):
if complete_path.endswith('.mat.gz'):
temp_filename = complete_path.split('.gz')[0]
with open(temp_filename, "wb") as tmp:
shutil.copyfileobj(gzip.open(complete_path), tmp)
dict_mr = sio.loadmat(temp_filename)
os.remove(temp_filename)
elif complete_path.endswith('.mat'):
dict_mr = sio.loadmat(complete_path)
else:
print('Unknown file extension for MountainRange file. Should be ' +
'.mat or .mat.gz')
self.value = dict_mr['value']
self.trigger_stamp = dict_mr['triggerStamp']
self.SC_numb = np.int(np.squeeze(dict_mr['superCycleNb']))
self.first_trigger_t_stamp_unix = dict_mr['first_trigger_t_stamp_unix']
self.sample_interval = float(np.squeeze(dict_mr['sampleInterval']))
self.first_sample_time = dict_mr['firstSampleTime']
self.sensitivity = dict_mr['sensitivity']
self.offset = dict_mr['offset']
self.SPSuser = dict_mr['SPSuser']
self.t_stamp_unix = dict_mr['t_stamp_unix']
self.time_axis = np.float_(range(self.value.shape[1]))*self.sample_interval-self.value.shape[1]*self.sample_interval/2.
def get_average_profile(self):
average_profile = np.mean(self.value[-20:,:],axis=0)
baseline = np.mean(average_profile[0:20])
average_profile -= baseline
return average_profile
def fit_sigma_t_first_bunch(self):
average_profile = self.get_average_profile()
mask_average_profile = self.time_axis < self.time_axis[np.argmax(average_profile)] + 1.2
mask_average_profile2 = self.time_axis > self.time_axis[np.argmax(average_profile)] - 1.2
mask_average_profile = mask_average_profile & mask_average_profile2
self._fitfunc = lambda p, x: p[0]*np.exp(-((x-p[1])**2)/(2.*p[2]**2))
self._errfunc = lambda p, x, y: self._fitfunc(p, x) - y # Distance to the target function
sigma0 = 1.
ampl = np.max(average_profile[mask_average_profile])
mu0 = self.time_axis[np.argmax(average_profile)]
p0 = [ampl, mu0, sigma0]
p1, success = op.leastsq(self._errfunc, p0[:], args=(self.time_axis[mask_average_profile],
average_profile[mask_average_profile]))
if success:
sigma_t = p1[2]
else:
sigma_t = 0.
ampl = 0.
mu0 = 0.
return sigma_t, ampl, mu0
def sdds_to_dict(in_complete_path):
try:
temp = sddsdata(in_complete_path, endian='little', full=True)
except IndexError:
print 'Failed to open data file. (save_mr_mat)'
return
data = temp.data[0]
superCycleNb = float(data['SCNumber'])
cycleTag = float(data['cycleTag'])
firstSampleTime = float(data['firstSampleTime'])
offset = float(data['offset'])
sampleInterval = float(data['sampleInterval'])
sensitivity = float(data['sensitivity'])
triggerError = np.float_(data['triggerError'])
triggerStamp = data['triggerStamp']
value = np.float_(data['value'])
#value_units = data['value_units'].tostring()
first_trigger_t_stamp_unix = float(triggerStamp[0])/1e9
SPSuser = in_complete_path.split('SPS.USER.')[-1].split('.')[0]
time_string_filename = in_complete_path.split('@')[3]
time_string_filename = ':'.join(time_string_filename.split('_')[:-1])
date_string_path = in_complete_path.split('/')[-3]
t_stamp_unix = time.mktime(time.strptime(date_string_path + ' ' + time_string_filename, '%Y_%m_%d %H:%M:%S'))
dict_meas = {
'superCycleNb': superCycleNb,
'cycleTag': cycleTag,
'firstSampleTime': firstSampleTime,
'offset': offset,
'sampleInterval': sampleInterval,
'sensitivity': sensitivity,
'triggerError': triggerError,
'triggerStamp': triggerStamp,
'value': value,
'first_trigger_t_stamp_unix': first_trigger_t_stamp_unix,
'SPSuser': SPSuser,
't_stamp_unix': t_stamp_unix }
return dict_meas
def sdds_to_file(in_complete_path, mat_filename_prefix='SPSmeas_', outp_folder='mr/'):
dict_meas = sdds_to_dict(in_complete_path)
us_string = dict_meas['SPSuser']
t_stamp_unix = dict_meas['t_stamp_unix']
out_filename = mat_filename_prefix + us_string + ('_%d'%t_stamp_unix)
out_complete_path = outp_folder + us_string +'/'+ out_filename
if not os.path.isdir(outp_folder + us_string):
print 'I create folder: '+ outp_folder + us_string
os.makedirs(outp_folder + us_string)
sio.savemat(out_complete_path, dict_meas, oned_as='row')
save_zip(out_complete_path)
def make_mat_files(start_time, end_time='Now', data_folder='/user/slops/data/SPS_DATA/OP_DATA/MR/',
SPSuser=None, filename_converted='mr_converted.txt'):
if type(start_time) is str:
start_tstamp_unix = th.localtime2unixstamp(start_time)
else:
start_tstamp_unix = start_time
if end_time == 'Now':
end_tstamp_unix = time.mktime(time.localtime())
elif type(end_time) is str:
end_tstamp_unix = th.localtime2unixstamp(end_time)
else:
end_tstamp_unix = end_time
try:
with open(filename_converted, 'r') as fid:
list_converted = fid.read().split('\n')
except IOError:
list_converted = []
list_date_strings = th.date_strings_interval(start_tstamp_unix, end_tstamp_unix)
sdds_folder_list = []
for date_string in list_date_strings:
sdds_folder_list.extend(glob.glob(data_folder + date_string + '/SMR.SCOPE13.CH01@Acquisition/'))
for sdds_folder in sdds_folder_list:
print '\nConverting data in folder: %s\n'%sdds_folder
file_list = os.listdir(sdds_folder)
for filename in file_list:
tstamp_filename = int(float(filename.split('@')[-2]) / 1e9)
if not(tstamp_filename > start_tstamp_unix and tstamp_filename < end_tstamp_unix):
continue
if SPSuser != None:
user_filename = filename.split('.')[-3]
print user_filename
if user_filename != SPSuser:
continue
if filename in list_converted:
continue
try:
complete_path = sdds_folder + filename
print complete_path
sdds_to_file(complete_path)
with open(filename_converted, 'a+') as fid:
fid.write(filename+'\n')
except Exception as err:
print 'Skipped:'
print complete_path
print 'Got exception:'
print err
def make_pickle(pickle_name='mr_overview.pkl', mat_folder='mr/'):
if os.path.isfile(pickle_name):
with open(pickle_name) as fid:
beams = pickle.load(fid)
print '\nUpdating file: %s'%pickle_name
else:
beams = {}
print '\nCreating file: %s'%pickle_name
SPSuser_list = os.listdir(mat_folder)
for SPSuser in SPSuser_list:
if not(SPSuser in beams.keys()):
beams[SPSuser] = {}
beams[SPSuser]['sigma_t'] = np.array([])
beams[SPSuser]['mu0'] = np.array([])
beams[SPSuser]['ampl'] = np.array([])
beams[SPSuser]['timestamp_float'] = np.array([])
list_mr_files = os.listdir(mat_folder +'/'+ SPSuser)
N_cycles = len(list_mr_files)
for ii in xrange(N_cycles):
filename_mr = list_mr_files[ii]
tstamp_mat_filename = float((filename_mr.split('_')[-1]).split('.mat')[0])
if tstamp_mat_filename in beams[SPSuser]['timestamp_float']:
continue
try:
print '%s %d/%d'%(SPSuser, ii, N_cycles - 1)
curr_mr = MountainRange(mat_folder +'/'+ SPSuser +'/'+ filename_mr)
SPSuser = curr_mr.SPSuser[0]
sigmat, ampl, mu0 = curr_mr.fit_sigma_t_first_bunch()
beams[SPSuser]['ampl'] = np.append(beams[SPSuser]['ampl'], ampl)
beams[SPSuser]['mu0'] = np.append(beams[SPSuser]['mu0'], mu0)
beams[SPSuser]['sigma_t'] = np.append(beams[SPSuser]['sigma_t'], sigmat)
beams[SPSuser]['timestamp_float'] = np.append(beams[SPSuser]['timestamp_float'], curr_mr.t_stamp_unix)
except IOError as err:
print err
ind_sorted = np.argsort(beams[SPSuser]['timestamp_float'])
for kk in beams[SPSuser].keys():
beams[SPSuser][kk] = np.take(beams[SPSuser][kk], ind_sorted)
with open(pickle_name, 'w') as fid:
pickle.dump(beams, fid)