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
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
|
#!/usr/bin/env python
"""Miscellaneous data features."""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "BSD"
from typing import TypeVar, Tuple, Any
import os
import importlib
from inspect import getmembers, getmodule
import collections
import json
import ast
import bisect
import threading
import math
import time
import pandas
import numpy
import matplotlib.pyplot as mpyplot
import matplotlib.patches as mpatches
TimeStampType = TypeVar('TimeStamp', int, float)
"""Type definition for timestamp as integer or float values."""
DataType = TypeVar('Data')
"""Type definition for data to store anything in time."""
TimeStampedBufferType = TypeVar('TimeStampedBuffer', bound="TimeStampedBuffer")
# Type definition for type annotation convenience
def module_path(obj) -> str:
"""
Get object module path.
Returns:
module path
"""
return obj.__class__.__module__
class JsonEncoder(json.JSONEncoder):
"""Specific ArGaze JSON Encoder."""
def default(self, obj):
"""default implementation to serialize object."""
# numpy cases
if isinstance(obj, numpy.integer):
return int(obj)
elif isinstance(obj, numpy.floating):
return float(obj)
elif isinstance(obj, numpy.ndarray):
return obj.tolist()
# default case
try:
return json.JSONEncoder.default(self, obj)
# class case
except:
# ignore attribute starting with _
public_dict = {}
for k, v in vars(obj).items():
if not k.startswith('_'):
# numpy cases
if isinstance(v, numpy.integer):
v = int(v)
elif isinstance(v, numpy.floating):
v = float(v)
elif isinstance(v, numpy.ndarray):
v = v.tolist()
public_dict[k] = v
return public_dict
class TimeStampedBuffer(collections.OrderedDict):
"""Ordered dictionary to handle timestamped data.
```
{
timestamp1: data1,
timestamp2: data2,
...
}
```
!!! warning
Timestamps must be numbers.
!!! warning "Timestamps are not sorted internally"
Data are considered to be stored according at their coming time.
"""
def __new__(cls, args = None):
"""Inheritance"""
return super(TimeStampedBuffer, cls).__new__(cls)
def __setitem__(self, ts: TimeStampType, data: DataType):
"""Store data at given timestamp."""
assert(type(ts) == int or type(ts) == float)
super().__setitem__(ts, data)
def __repr__(self):
"""String representation"""
return json.dumps(self, ensure_ascii=False, cls=JsonEncoder)
def __str__(self):
"""String representation"""
return json.dumps(self, ensure_ascii=False, cls=JsonEncoder)
def append(self, timestamped_buffer: TimeStampedBufferType) -> TimeStampedBufferType:
"""Append a timestamped buffer."""
for ts, value in timestamped_buffer.items():
self[ts] = value
return self
@property
def first(self) -> Tuple[TimeStampType, DataType]:
"""Easing access to first item."""
return list(self.items())[0]
def pop_first(self) -> Tuple[TimeStampType, DataType]:
"""Easing FIFO access mode."""
return self.popitem(last=False)
def pop_last_until(self, ts: TimeStampType) -> Tuple[TimeStampType, DataType]:
"""Pop all item until a given timestamped value and return the first after."""
# get last item before given timestamp
earliest_ts, earliest_value = self.get_last_until(ts)
first_ts, first_value = self.first
while first_ts < earliest_ts:
self.pop_first()
first_ts, first_value = self.first
return first_ts, first_value
def pop_last_before(self, ts: TimeStampType) -> Tuple[TimeStampType, DataType]:
"""Pop all item before a given timestamped value and return the last one."""
# get last item before given timestamp
earliest_ts, earliest_value = self.get_last_before(ts)
popep_ts, poped_value = self.pop_first()
while popep_ts != earliest_ts:
popep_ts, poped_value = self.pop_first()
return popep_ts, poped_value
@property
def last(self) -> Tuple[TimeStampType, DataType]:
"""Easing access to last item."""
return list(self.items())[-1]
def pop_last(self) -> Tuple[TimeStampType, DataType]:
"""Easing FIFO access mode."""
return self.popitem(last=True)
def get_first_from(self, ts) -> Tuple[TimeStampType, DataType]:
"""Retreive first item timestamp from a given timestamp value."""
ts_list = list(self.keys())
first_from_index = bisect.bisect_left(ts_list, ts)
if first_from_index < len(self):
first_from_ts = ts_list[first_from_index]
return first_from_ts, self[first_from_ts]
else:
raise KeyError(f'No data stored after {ts} timestamp.')
def get_last_before(self, ts) -> Tuple[TimeStampType, DataType]:
"""Retreive last item timestamp before a given timestamp value."""
ts_list = list(self.keys())
last_before_index = bisect.bisect_left(ts_list, ts) - 1
if last_before_index >= 0:
last_before_ts = ts_list[last_before_index]
return last_before_ts, self[last_before_ts]
else:
raise KeyError(f'No data stored before {ts} timestamp.')
def get_last_until(self, ts) -> Tuple[TimeStampType, DataType]:
"""Retreive last item timestamp until a given timestamp value."""
ts_list = list(self.keys())
last_until_index = bisect.bisect_right(ts_list, ts) - 1
if last_until_index >= 0:
last_until_ts = ts_list[last_until_index]
return last_until_ts, self[last_until_ts]
else:
raise KeyError(f'No data stored until {ts} timestamp.')
@classmethod
def from_json(self, json_filepath: str) -> TimeStampedBufferType:
"""Create a TimeStampedBuffer from .json file."""
with open(json_filepath, encoding='utf-8') as ts_buffer_file:
json_buffer = json.load(ts_buffer_file)
return TimeStampedBuffer({ast.literal_eval(ts_str): json_buffer[ts_str] for ts_str in json_buffer})
def to_json(self, json_filepath: str):
"""Save a TimeStampedBuffer to .json file."""
with open(json_filepath, 'w', encoding='utf-8') as ts_buffer_file:
json.dump(self, ts_buffer_file, ensure_ascii=False, cls=JsonEncoder)
@classmethod
def from_dataframe(self, dataframe: pandas.DataFrame, exclude=[]) -> TimeStampedBufferType:
"""Create a TimeStampedBuffer from [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)."""
dataframe.drop(exclude, inplace=True, axis=True)
assert(dataframe.index.name == 'timestamp')
return TimeStampedBuffer(dataframe.to_dict('index'))
def as_dataframe(self, exclude=[], split={}) -> pandas.DataFrame:
"""Convert as [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html).
The optional *split* argument allows tuple values to be stored in dedicated columns.
For example: to convert {"point": (0, 0)} data as two separated "x" and "y" columns, use split={"point": ["x", "y"]}
!!! warning "Values must be dictionaries"
Each key is stored as a column name.
!!! note
Timestamps are stored as index column called 'timestamp'.
"""
df = pandas.DataFrame.from_dict(self.values())
# Exclude columns
df.drop(exclude, inplace=True, axis=True)
# Split columns
if len(split) > 0:
splited_columns = []
for column in df.columns:
if column in split.keys():
df[split[column]] = pandas.DataFrame(df[column].tolist(), index=df.index)
df.drop(column, inplace=True, axis=True)
for new_column in split[column]:
splited_columns.append(new_column)
else:
splited_columns.append(column)
# Reorder splited columns
df = df[splited_columns]
# Append timestamps as index column
df['timestamp'] = self.keys()
df.set_index('timestamp', inplace=True)
return df
def plot(self, names=[], colors=[], split={}, samples=None) -> list:
"""Plot as [matplotlib](https://matplotlib.org/) time chart."""
df = self.as_dataframe(split=split)
legend_patches = []
# decimate data
if samples != None:
if samples < len(df):
step = int(len(df) / samples) + 1
df = df.iloc[::step, :]
for name, color in zip(names, colors):
markerline, stemlines, baseline = mpyplot.stem(df.index, df[name])
mpyplot.setp(markerline, color=color, linewidth=1, markersize = 1)
mpyplot.setp(stemlines, color=color, linewidth=1)
mpyplot.setp(baseline, color=color, linewidth=1)
legend_patches.append(mpatches.Patch(color=color, label=name.upper()))
return legend_patches
class DataDictionary(dict):
"""Enable dot.notation access to dictionary attributes"""
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
class SharedObject():
"""Abstract class to enable multiple threads sharing and timestamp management."""
def __init__(self):
# DEBUG
print('SharedObject.__init__')
self._lock = threading.Lock()
self._timestamp = math.nan
self._execution_times = {}
self._exceptions = {}
@property
def lock(self) -> threading.Lock:
"""Get shared object lock object."""
return self._lock
@property
def timestamp(self) -> int|float:
"""Get shared object timestamp."""
self._lock.acquire()
timestamp = self._timestamp
self._lock.release()
return timestamp
@timestamp.setter
def timestamp(self, timestamp: int|float):
"""Set shared object timestamp."""
self._lock.acquire()
self._timestamp = timestamp
self._lock.release()
def untimestamp(self):
"""Reset shared object timestamp."""
self._lock.acquire()
self._timestamp = math.nan
self._lock.release()
@property
def timestamped(self) -> bool:
"""Is the object timestamped?"""
self._lock.acquire()
timestamped = not math.isnan(self._timestamp)
self._lock.release()
return timestamped
class PipelineStepObject():
"""
Define class to assess pipeline step methods execution time and observe them.
"""
def __init__(self, observers: dict = None):
"""Initialize PipelineStepObject
Parameters:
observers: dictionary with observers objects.
"""
# DEBUG
print('PipelineStepObject.__init__', observers)
# Init private attribute
self.__observers = observers if observers is not None else {}
self.__execution_times = {}
@property
def observers(self) -> dict:
"""Get pipeline step object observers dictionary."""
return self.__observers
@property
def execution_times(self):
"""Get pipeline step object observers execution times dictionary."""
return self.__execution_times
def as_dict(self) -> dict:
"""Export PipelineStepObject attributes as dictionary.
Returns:
object_data: dictionary with pipeline step object attributes values.
"""
return {
"observers": self.__observers,
}
@classmethod
def from_dict(self, object_data: dict, working_directory: str = None) -> object:
"""Load PipelineStepObject attributes from dictionary.
Returns:
object_data: dictionary with pipeline step object attributes values.
working_directory: folder path where to load files when a dictionary value is a relative filepath.
"""
# Load observers
new_observers = {}
try:
new_observers_value = object_data.pop('observers')
# str: relative path to file
if type(new_observers_value) == str:
filepath = os.path.join(working_directory, new_observers_value)
file_format = filepath.split('.')[-1]
# Python file format
if file_format == 'py':
observer_module_path = new_observers_value.split('.')[0]
observer_module = importlib.import_module(observer_module_path)
new_observers = observer_module.__observers__
except KeyError:
pass
# Create pipeline step
return PipelineStepObject(new_observers)
@classmethod
def from_json(self, json_filepath: str) -> object:
"""
Define abstract method to load PipelineStepObject from .json file.
Parameters:
json_filepath: path to json file
"""
raise NotImplementedError('from_json() method not implemented')
def __str__(self) -> str:
"""
Define abstract method to have a string representation of PipelineStepObject.
Returns:
String representation
"""
raise NotImplementedError('__str__() method not implemented')
def PipelineStepAttribute(method):
# Mark method as
method._tags = tags
return method
# DEBUG
from argaze import ArFeatures
def PipelineStepMethod(method):
"""Define a decorator use into PipelineStepObject class to declare pipeline method.
!!! danger
PipelineStepMethod must have a timestamp as first argument.
"""
def wrapper(self, timestamp, *args, **kw):
"""Wrap pipeline step method to measure execution time."""
# DEBUG
if type(self) == ArFeatures.ArFrame:
print(timestamp, self.name, method.__name__, len(self.observers))
# Initialize execution time assessment
start = time.perf_counter()
try:
# Execute wrapped method
result = method(self, timestamp, *args, **kw)
finally:
# Measure execution time
self.execution_times[method.__name__] = (time.perf_counter() - start) * 1e3
# Notify observers that method has been called
subscription_name = f'on_{method.__name__}'
for observer_name, observer in self.observers.items():
# Does the observer cares about this method?
if subscription_name in dir(observer):
subscription = getattr(observer, subscription_name)
# Call subscription
subscription(timestamp, self)
return result
return wrapper
class PipelineStepObserver():
"""Define abstract class to observe pipeline step object use.
!!! note
To subscribe to a method call, the inherited class simply needs to define 'on_<method_name>' functions with timestamp and object argument.
"""
|