Coverage for python/lsst/summit/utils/tmaUtils.py: 20%
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1# This file is part of summit_utils.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22import re
23import enum
24import itertools
25import logging
26import pandas as pd
27import numpy as np
28import humanize
29from dataclasses import dataclass
30from astropy.time import Time
31from matplotlib.ticker import FuncFormatter
32import matplotlib.dates as mdates
33import matplotlib.pyplot as plt
34from lsst.utils.iteration import ensure_iterable
36from .enums import AxisMotionState, PowerState
37from .blockUtils import BlockParser
38from .utils import getCurrentDayObs_int, dayObsIntToString
39from .efdUtils import (getEfdData,
40 makeEfdClient,
41 efdTimestampToAstropy,
42 COMMAND_ALIASES,
43 getDayObsForTime,
44 getDayObsStartTime,
45 getDayObsEndTime,
46 )
48__all__ = (
49 'TMAStateMachine',
50 'TMAEvent',
51 'TMAEventMaker',
52 'TMAState',
53 'AxisMotionState',
54 'PowerState',
55 'getSlewsFromEventList',
56 'getTracksFromEventList',
57 'getTorqueMaxima',
58)
60# we don't want to use `None` for a no data sentinel because dict.get('key')
61# returns None if the key isn't present, and also we need to mark that the data
62# was queried for and no data was found, whereas the key not being present
63# means that we've not yet looked for the data.
64NO_DATA_SENTINEL = "NODATA"
67def getSlewsFromEventList(events):
68 """Get the slew events from a list of TMAEvents.
70 Parameters
71 ----------
72 events : `list` of `lsst.summit.utils.tmaUtils.TMAEvent`
73 The list of events to filter.
75 Returns
76 -------
77 events : `list` of `lsst.summit.utils.tmaUtils.TMAEvent`
78 The filtered list of events.
79 """
80 return [e for e in events if e.type == TMAState.SLEWING]
83def getTracksFromEventList(events):
84 """Get the tracking events from a list of TMAEvents.
86 Parameters
87 ----------
88 events : `list` of `lsst.summit.utils.tmaUtils.TMAEvent`
89 The list of events to filter.
91 Returns
92 -------
93 events : `list` of `lsst.summit.utils.tmaUtils.TMAEvent`
94 The filtered list of events.
95 """
96 return [e for e in events if e.type == TMAState.TRACKING]
99def getTorqueMaxima(table):
100 """Print the maximum positive and negative azimuth and elevation torques.
102 Designed to be used with the table as downloaded from RubinTV.
104 Parameters
105 ----------
106 table : `pd.DataFrame`
107 The table of data to use, as generated by Rapid Analysis.
108 """
109 for axis in ['elevation', 'azimuth']:
110 col = f'Largest {axis} torque'
111 maxPos = np.argmax(table[col])
112 maxVal = table[col].iloc[maxPos]
113 print(f"Max positive {axis:9} torque during seqNum {maxPos:>4}: {maxVal/1000:>7.1f}kNm")
114 minPos = np.argmin(table[col])
115 minVal = table[col].iloc[minPos]
116 print(f"Max negative {axis:9} torque during seqNum {minPos:>4}: {minVal/1000:>7.1f}kNm")
119def getAzimuthElevationDataForEvent(client, event, prePadding=0, postPadding=0):
120 """Get the data for the az/el telemetry topics for a given TMAEvent.
122 Parameters
123 ----------
124 client : `lsst_efd_client.efd_helper.EfdClient`
125 The EFD client to use.
126 event : `lsst.summit.utils.tmaUtils.TMAEvent`
127 The event to get the data for.
128 prePadding : `float`, optional
129 The amount of time to pad the event with before the start time, in
130 seconds.
131 postPadding : `float`, optional
132 The amount of time to pad the event with after the end time, in
133 seconds.
135 Returns
136 -------
137 azimuthData : `pd.DataFrame`
138 The azimuth data for the specified event.
139 elevationData : `pd.DataFrame`
140 The elevation data for the specified event.
141 """
142 azimuthData = getEfdData(client,
143 'lsst.sal.MTMount.azimuth',
144 event=event,
145 prePadding=prePadding,
146 postPadding=postPadding)
147 elevationData = getEfdData(client,
148 'lsst.sal.MTMount.elevation',
149 event=event,
150 prePadding=prePadding,
151 postPadding=postPadding)
153 return azimuthData, elevationData
156def plotEvent(client, event, fig=None, prePadding=0, postPadding=0, commands={},
157 azimuthData=None, elevationData=None):
158 """Plot the TMA axis positions over the course of a given TMAEvent.
160 Plots the axis motion profiles for the given event, with optional padding
161 at the start and end of the event. If the data is provided via the
162 azimuthData and elevationData parameters, it will be used, otherwise it
163 will be queried from the EFD.
165 Optionally plots any commands issued during or around the event, if these
166 are supplied. Commands are supplied as a dictionary of the command topic
167 strings, with values as astro.time.Time objects at which the command was
168 issued.
170 Parameters
171 ----------
172 client : `lsst_efd_client.efd_helper.EfdClient`
173 The EFD client to use.
174 event : `lsst.summit.utils.tmaUtils.TMAEvent`
175 The event to plot.
176 fig : `matplotlib.figure.Figure`, optional
177 The figure to plot on. If not specified, a new figure will be created.
178 prePadding : `float`, optional
179 The amount of time to pad the event with before the start time, in
180 seconds.
181 postPadding : `float`, optional
182 The amount of time to pad the event with after the end time, in
183 seconds.
184 commands : `dict` of `str` : `astropy.time.Time`, optional
185 A dictionary of commands to plot on the figure. The keys are the topic
186 names, and the values are the times at which the commands were sent.
187 azimuthData : `pd.DataFrame`, optional
188 The azimuth data to plot. If not specified, it will be queried from the
189 EFD.
190 elevationData : `pd.DataFrame`, optional
191 The elevation data to plot. If not specified, it will be queried from
192 the EFD.
194 Returns
195 -------
196 fig : `matplotlib.figure.Figure`
197 The figure on which the plot was made.
198 """
199 def tickFormatter(value, tick_number):
200 # Convert the value to a string without subtracting large numbers
201 # tick_number is unused.
202 return f"{value:.2f}"
204 # plot any commands we might have
205 if not isinstance(commands, dict):
206 raise TypeError('commands must be a dict of command names with values as'
207 ' astropy.time.Time values')
209 if fig is None:
210 fig = plt.figure(figsize=(10, 8))
211 log = logging.getLogger(__name__)
212 log.warning("Making new matplotlib figure - if this is in a loop you're going to have a bad time."
213 " Pass in a figure with fig = plt.figure(figsize=(10, 8)) to avoid this warning.")
215 fig.clear()
216 ax1, ax2 = fig.subplots(2,
217 sharex=True,
218 gridspec_kw={'wspace': 0,
219 'hspace': 0,
220 'height_ratios': [2.5, 1]})
222 if azimuthData is None or elevationData is None:
223 azimuthData, elevationData = getAzimuthElevationDataForEvent(client,
224 event,
225 prePadding=prePadding,
226 postPadding=postPadding)
228 # Use the native color cycle for the lines. Because they're on different
229 # axes they don't cycle by themselves
230 lineColors = [p['color'] for p in plt.rcParams['axes.prop_cycle']]
231 colorCounter = 0
233 ax1.plot(azimuthData['actualPosition'], label='Azimuth position', c=lineColors[colorCounter])
234 colorCounter += 1
235 ax1.yaxis.set_major_formatter(FuncFormatter(tickFormatter))
236 ax1.set_ylabel('Azimuth (degrees)')
238 ax1_twin = ax1.twinx()
239 ax1_twin.plot(elevationData['actualPosition'], label='Elevation position', c=lineColors[colorCounter])
240 colorCounter += 1
241 ax1_twin.yaxis.set_major_formatter(FuncFormatter(tickFormatter))
242 ax1_twin.set_ylabel('Elevation (degrees)')
243 ax1.set_xticks([]) # remove x tick labels on the hidden upper x-axis
245 ax2_twin = ax2.twinx()
246 ax2.plot(azimuthData['actualTorque'], label='Azimuth torque', c=lineColors[colorCounter])
247 colorCounter += 1
248 ax2_twin.plot(elevationData['actualTorque'], label='Elevation torque', c=lineColors[colorCounter])
249 colorCounter += 1
250 ax2.set_ylabel('Azimuth torque (Nm)')
251 ax2_twin.set_ylabel('Elevation torque (Nm)')
252 ax2.set_xlabel('Time (UTC)') # yes, it really is UTC, matplotlib converts this automatically!
254 # put the ticks at an angle, and right align with the tick marks
255 ax2.set_xticks(ax2.get_xticks()) # needed to supress a user warning
256 xlabels = ax2.get_xticks()
257 ax2.set_xticklabels(xlabels, rotation=40, ha='right')
258 ax2.xaxis.set_major_locator(mdates.AutoDateLocator())
259 ax2.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
261 if prePadding or postPadding:
262 # note the conversion to utc because the x-axis from the dataframe
263 # already got automagically converted when plotting before, so this is
264 # necessary for things to line up
265 ax1_twin.axvline(event.begin.utc.datetime, c='k', ls='--', alpha=0.5, label='Event begin/end')
266 ax1_twin.axvline(event.end.utc.datetime, c='k', ls='--', alpha=0.5)
267 # extend lines down across lower plot, but do not re-add label
268 ax2_twin.axvline(event.begin.utc.datetime, c='k', ls='--', alpha=0.5)
269 ax2_twin.axvline(event.end.utc.datetime, c='k', ls='--', alpha=0.5)
271 for command, commandTime in commands.items():
272 # if commands weren't found, the item is set to None. This is common
273 # for events so handle it gracefully and silently. The command finding
274 # code logs about lack of commands found so no need to mention here.
275 if commandTime is None:
276 continue
277 ax1_twin.axvline(commandTime.utc.datetime, c=lineColors[colorCounter],
278 ls='--', alpha=0.75, label=f'{command}')
279 # extend lines down across lower plot, but do not re-add label
280 ax2_twin.axvline(commandTime.utc.datetime, c=lineColors[colorCounter],
281 ls='--', alpha=0.75)
282 colorCounter += 1
284 # combine the legends and put inside the plot
285 handles1a, labels1a = ax1.get_legend_handles_labels()
286 handles1b, labels1b = ax1_twin.get_legend_handles_labels()
287 handles2a, labels2a = ax2.get_legend_handles_labels()
288 handles2b, labels2b = ax2_twin.get_legend_handles_labels()
290 handles = handles1a + handles1b + handles2a + handles2b
291 labels = labels1a + labels1b + labels2a + labels2b
292 # ax2 is "in front" of ax1 because it has the vlines plotted on it, and
293 # vlines are on ax2 so that they appear at the bottom of the legend, so
294 # make sure to plot the legend on ax2, otherwise the vlines will go on top
295 # of the otherwise-opaque legend.
296 ax1_twin.legend(handles, labels, facecolor='white', framealpha=1)
298 # Add title with the event name, type etc
299 dayObsStr = dayObsIntToString(event.dayObs)
300 title = (f"{dayObsStr} - seqNum {event.seqNum} (version {event.version})" # top line, rest below
301 f"\nDuration = {event.duration:.2f}s"
302 f" Event type: {event.type.name}"
303 f" End reason: {event.endReason.name}"
304 )
305 ax1_twin.set_title(title)
306 return fig
309def getCommandsDuringEvent(client, event, commands=('raDecTarget'), log=None, doLog=True):
310 """Get the commands issued during an event.
312 Get the times at which the specified commands were issued during the event.
314 Parameters
315 ----------
316 client : `lsst_efd_client.efd_helper.EfdClient`
317 The EFD client to use.
318 event : `lsst.summit.utils.tmaUtils.TMAEvent`
319 The event to plot.
320 commands : `list` of `str`, optional
321 The commands or command aliases to look for. Defaults to
322 ['raDecTarget'].
323 log : `logging.Logger`, optional
324 The logger to use. If not specified, a new logger will be created if
325 needed.
326 doLog : `bool`, optional
327 Whether to log messages. Defaults to True.
329 Returns
330 -------
331 commands : `dict` of `str` : `astropy.time.Time`
332 A dictionary of the commands and the times at which they were issued.
333 """
334 # TODO: DM-40100 Add support for padding the event here to allow looking
335 # for triggering commands before the event
337 # TODO: DM-40100 Change this to always return a list of times, and remove
338 # warning about finding multiple commands. Remember to update docs and
339 # plotting code.
340 if log is None and doLog:
341 log = logging.getLogger(__name__)
343 commands = ensure_iterable(commands)
344 fullCommands = [c if c not in COMMAND_ALIASES else COMMAND_ALIASES[c] for c in commands]
345 del commands # make sure we always use their full names
347 ret = {}
348 for command in fullCommands:
349 data = getEfdData(client, command, event=event, warn=False)
350 if data.empty:
351 if doLog:
352 log.info(f'Found no command issued for {command} during event')
353 ret[command] = None
354 elif len(data) > 1:
355 if doLog:
356 log.warning(f'Found multiple commands issued for {command} during event, returning None')
357 ret[command] = None
358 else:
359 assert len(data) == 1 # this must be true now
360 commandTime = data.private_efdStamp
361 ret[command] = Time(commandTime, format='unix')
363 return ret
366def _initializeTma(tma):
367 """Helper function to turn a TMA into a valid state for testing.
369 Do not call directly in normal usage or code, as this just arbitrarily
370 sets values to make the TMA valid.
372 Parameters
373 ----------
374 tma : `lsst.summit.utils.tmaUtils.TMAStateMachine`
375 The TMA state machine model to initialize.
376 """
377 tma._parts['azimuthInPosition'] = False
378 tma._parts['azimuthMotionState'] = AxisMotionState.STOPPED
379 tma._parts['azimuthSystemState'] = PowerState.ON
380 tma._parts['elevationInPosition'] = False
381 tma._parts['elevationMotionState'] = AxisMotionState.STOPPED
382 tma._parts['elevationSystemState'] = PowerState.ON
385@dataclass(kw_only=True, frozen=True)
386class TMAEvent:
387 """A movement event for the TMA.
389 Contains the dayObs on which the event occured, using the standard
390 observatory definition of the dayObs, and the sequence number of the event,
391 which is unique for each event on a given dayObs.
393 The event type can be either 'SLEWING' or 'TRACKING', defined as:
394 - SLEWING: some part of the TMA is in motion
395 - TRACKING: both axes are in position and tracking the sky
397 The end reason can be 'STOPPED', 'TRACKING', 'FAULT', 'SLEWING', or 'OFF'.
398 - SLEWING: The previous event was a TRACKING event, and one or more of
399 the TMA components either stopped being in position, or stopped
400 moving, or went into fault, or was turned off, and hence we are now
401 only slewing and no longer tracking the sky.
402 - TRACKING: the TMA started tracking the sky when it wasn't previously.
403 Usualy this would always be preceded by directly by a SLEWING
404 event, but this is not strictly true, as the EUI seems to be able
405 to make the TMA start tracking the sky without slewing first.
406 - STOPPED: the components of the TMA transitioned to the STOPPED state.
407 - FAULT: the TMA went into fault.
408 - OFF: the TMA components were turned off.
410 Note that this class is not intended to be instantiated directly, but
411 rather to be returned by the ``TMAEventMaker.getEvents()`` function.
413 Parameters
414 ----------
415 dayObs : `int`
416 The dayObs on which the event occured.
417 seqNum : `int`
418 The sequence number of the event,
419 type : `lsst.summit.utils.tmaUtils.TMAState`
420 The type of the event, either 'SLEWING' or 'TRACKING'.
421 endReason : `lsst.summit.utils.tmaUtils.TMAState`
422 The reason the event ended, either 'STOPPED', 'TRACKING', 'FAULT',
423 'SLEWING', or 'OFF'.
424 duration : `float`
425 The duration of the event, in seconds.
426 begin : `astropy.time.Time`
427 The time the event began.
428 end : `astropy.time.Time`
429 The time the event ended.
430 blockInfos : `list` of `lsst.summit.utils.tmaUtils.BlockInfo`, or `None`
431 The block infomation, if any, relating to the event. Could be `None`,
432 or one or more block informations.
433 version : `int`
434 The version of the TMAEvent class. Equality between events is only
435 valid for a given version of the class. If the class definition
436 changes, the time ranges can change, and hence the equality between
437 events is ``False``.
438 _startRow : `int`
439 The first row in the merged EFD data which is part of the event.
440 _endRow : `int`
441 The last row in the merged EFD data which is part of the event.
442 """
443 dayObs: int
444 seqNum: int
445 type: str # can be 'SLEWING', 'TRACKING'
446 endReason: str # can be 'STOPPED', 'TRACKING', 'FAULT', 'SLEWING', 'OFF'
447 duration: float # seconds
448 begin: Time
449 end: Time
450 blockInfos: list = None
451 version: int = 0 # update this number any time a code change which could change event definitions is made
452 _startRow: int
453 _endRow: int
455 def __lt__(self, other):
456 if self.version != other.version:
457 raise ValueError(
458 f"Cannot compare TMAEvents with different versions: {self.version} != {other.version}"
459 )
460 if self.dayObs < other.dayObs:
461 return True
462 elif self.dayObs == other.dayObs:
463 return self.seqNum < other.seqNum
464 return False
466 def __repr__(self):
467 return (
468 f"TMAEvent(dayObs={self.dayObs}, seqNum={self.seqNum}, type={self.type!r},"
469 f" endReason={self.endReason!r}, duration={self.duration}, begin={self.begin!r},"
470 f" end={self.end!r}"
471 )
473 def _ipython_display_(self):
474 print(self.__str__())
476 def __str__(self):
477 def indent(string):
478 return '\n' + '\n'.join([' ' + s for s in string.splitlines()])
480 blockInfoStr = 'None'
481 if self.blockInfos is not None:
482 blockInfoStr = ''.join(indent(str(i)) for i in self.blockInfos)
484 return (
485 f"dayObs: {self.dayObs}\n"
486 f"seqNum: {self.seqNum}\n"
487 f"type: {self.type.name}\n"
488 f"endReason: {self.endReason.name}\n"
489 f"duration: {self.duration}\n"
490 f"begin: {self.begin!r}\n"
491 f"end: {self.end!r}\n"
492 f"blockInfos: {blockInfoStr}"
493 )
496class TMAState(enum.IntEnum):
497 """Overall state of the TMA.
499 States are defined as follows:
501 UNINITIALIZED
502 We have not yet got data for all relevant components, so the overall
503 state is undefined.
504 STOPPED
505 All components are on, and none are moving.
506 TRACKING
507 We are tracking the sky.
508 SLEWING
509 One or more components are moving, and one or more are not tracking the
510 sky. This should probably be called MOVING, as it includes: slewing,
511 MOVING_POINT_TO_POINT, and JOGGING.
512 FAULT
513 All (if engineeringMode) or any (if not engineeringMode) components are
514 in fault.
515 OFF
516 All components are off.
517 """
518 UNINITIALIZED = -1
519 STOPPED = 0
520 TRACKING = 1
521 SLEWING = 2
522 FAULT = 3
523 OFF = 4
525 def __repr__(self):
526 return f"TMAState.{self.name}"
529def getAxisAndType(rowFor):
530 """Get the axis the data relates to, and the type of data it contains.
532 Parameters
533 ----------
534 rowFor : `str`
535 The column in the dataframe denoting what this row is for, e.g.
536 "elevationMotionState" or "azimuthInPosition", etc.
538 Returns
539 -------
540 axis : `str`
541 The axis the row is for, e.g. "azimuth", "elevation".
542 rowType : `str`
543 The type of the row, e.g. "MotionState", "SystemState", "InPosition".
544 """
545 regex = r'(azimuth|elevation)(InPosition|MotionState|SystemState)$' # matches the end of the line
546 matches = re.search(regex, rowFor)
547 if matches is None:
548 raise ValueError(f"Could not parse axis and rowType from {rowFor=}")
549 axis = matches.group(1)
550 rowType = matches.group(2)
552 assert rowFor.endswith(f"{axis}{rowType}")
553 return axis, rowType
556class ListViewOfDict:
557 """A class to allow making lists which contain references to an underlying
558 dictionary.
560 Normally, making a list of items from a dictionary would make a copy of the
561 items, but this class allows making a list which contains references to the
562 underlying dictionary items themselves. This is useful for making a list of
563 components, such that they can be manipulated in their logical sets.
564 """
565 def __init__(self, underlyingDictionary, keysToLink):
566 self.dictionary = underlyingDictionary
567 self.keys = keysToLink
569 def __getitem__(self, index):
570 return self.dictionary[self.keys[index]]
572 def __setitem__(self, index, value):
573 self.dictionary[self.keys[index]] = value
575 def __len__(self):
576 return len(self.keys)
579class TMAStateMachine:
580 """A state machine model of the TMA.
582 Note that this is currently only implemented for the azimuth and elevation
583 axes, but will be extended to include the rotator in the future.
585 Note that when used for event generation, changing ``engineeringMode`` to
586 False might change the resulting list of events, and that if the TMA moves
587 with some axis in fault, then these events will be missed. It is therefore
588 thought that ``engineeringMode=True`` should always be used when generating
589 events. The option, however, is there for completeness, as this will be
590 useful for knowing is the CSC would consider the TMA to be in fault in the
591 general case.
593 Parameters
594 ----------
595 engineeringMode : `bool`, optional
596 Whether the TMA is in engineering mode. Defaults to True. If False,
597 then the TMA will be in fault if any component is in fault. If True,
598 then the TMA will be in fault only if all components are in fault.
599 debug : `bool`, optional
600 Whether to log debug messages. Defaults to False.
601 """
602 _UNINITIALIZED_VALUE: int = -999
604 def __init__(self, engineeringMode=True, debug=False):
605 self.engineeringMode = engineeringMode
606 self.log = logging.getLogger('lsst.summit.utils.tmaUtils.TMA')
607 if debug:
608 self.log.level = logging.DEBUG
609 self._mostRecentRowTime = -1
611 # the actual components of the TMA
612 self._parts = {'azimuthInPosition': self._UNINITIALIZED_VALUE,
613 'azimuthMotionState': self._UNINITIALIZED_VALUE,
614 'azimuthSystemState': self._UNINITIALIZED_VALUE,
615 'elevationInPosition': self._UNINITIALIZED_VALUE,
616 'elevationMotionState': self._UNINITIALIZED_VALUE,
617 'elevationSystemState': self._UNINITIALIZED_VALUE,
618 }
619 systemKeys = ['azimuthSystemState', 'elevationSystemState']
620 positionKeys = ['azimuthInPosition', 'elevationInPosition']
621 motionKeys = ['azimuthMotionState', 'elevationMotionState']
623 # references to the _parts as conceptual groupings
624 self.system = ListViewOfDict(self._parts, systemKeys)
625 self.motion = ListViewOfDict(self._parts, motionKeys)
626 self.inPosition = ListViewOfDict(self._parts, positionKeys)
628 # tuples of states for state collapsing. Note that STOP_LIKE +
629 # MOVING_LIKE must cover the full set of AxisMotionState enums
630 self.STOP_LIKE = (AxisMotionState.STOPPING,
631 AxisMotionState.STOPPED,
632 AxisMotionState.TRACKING_PAUSED)
633 self.MOVING_LIKE = (AxisMotionState.MOVING_POINT_TO_POINT,
634 AxisMotionState.JOGGING,
635 AxisMotionState.TRACKING)
636 # Likewise, ON_LIKE + OFF_LIKE must cover the full set of PowerState
637 # enums
638 self.OFF_LIKE = (PowerState.OFF, PowerState.TURNING_OFF)
639 self.ON_LIKE = (PowerState.ON, PowerState.TURNING_ON)
640 self.FAULT_LIKE = (PowerState.FAULT,) # note the trailing comma - this must be an iterable
642 def apply(self, row):
643 """Apply a row of data to the TMA state.
645 Checks that the row contains data for a later time than any data
646 previously applied, and applies the relevant column entry to the
647 relevant component.
649 Parameters
650 ----------
651 row : `pd.Series`
652 The row of data to apply to the state machine.
653 """
654 timestamp = row['private_efdStamp']
655 if timestamp < self._mostRecentRowTime: # NB equals is OK, technically, though it never happens
656 raise ValueError('TMA evolution must be monotonic increasing in time, tried to apply a row which'
657 ' predates the most previous one')
658 self._mostRecentRowTime = timestamp
660 rowFor = row['rowFor'] # e.g. elevationMotionState
661 axis, rowType = getAxisAndType(rowFor) # e.g. elevation, MotionState
662 value = self._getRowPayload(row, rowType, rowFor)
663 self.log.debug(f"Setting {rowFor} to {repr(value)}")
664 self._parts[rowFor] = value
665 try:
666 # touch the state property as this executes the sieving, to make
667 # sure we don't fall through the sieve at any point in time
668 _ = self.state
669 except RuntimeError as e:
670 # improve error reporting, but always reraise this, as this is a
671 # full-blown failure
672 raise RuntimeError(f'Failed to apply {value} to {axis}{rowType} with state {self._parts}') from e
674 def _getRowPayload(self, row, rowType, rowFor):
675 """Get the relevant value from the row.
677 Given the row, and which component it relates to, get the relevant
678 value, as a bool or cast to the appropriate enum class.
680 Parameters
681 ----------
682 row : `pd.Series`
683 The row of data from the dataframe.
684 rowType : `str`
685 The type of the row, e.g. "MotionState", "SystemState",
686 "InPosition".
687 rowFor : `str`
688 The component the row is for, e.g. "azimuth", "elevation".
690 Returns
691 -------
692 value : `bool` or `enum`
693 The value of the row, as a bool or enum, depending on the
694 component, cast to the appropriate enum class or bool.
695 """
696 match rowType:
697 case 'MotionState':
698 value = row[f'state_{rowFor}']
699 return AxisMotionState(value)
700 case 'SystemState':
701 value = row[f'powerState_{rowFor}']
702 return PowerState(value)
703 case 'InPosition':
704 value = row[f'inPosition_{rowFor}']
705 return bool(value)
706 case _:
707 raise ValueError(f'Failed to get row payload with {rowType=} and {row=}')
709 @property
710 def _isValid(self):
711 """Has the TMA had a value applied to all its components?
713 If any component has not yet had a value applied, the TMA is not valid,
714 as those components will be in an unknown state.
716 Returns
717 -------
718 isValid : `bool`
719 Whether the TMA is fully initialized.
720 """
721 return not any([v == self._UNINITIALIZED_VALUE for v in self._parts.values()])
723 # state inspection properties - a high level way of inspecting the state as
724 # an API
725 @property
726 def isMoving(self):
727 return self.state in [TMAState.TRACKING, TMAState.SLEWING]
729 @property
730 def isNotMoving(self):
731 return not self.isMoving
733 @property
734 def isTracking(self):
735 return self.state == TMAState.TRACKING
737 @property
738 def isSlewing(self):
739 return self.state == TMAState.SLEWING
741 @property
742 def canMove(self):
743 badStates = [PowerState.OFF, PowerState.TURNING_OFF, PowerState.FAULT, PowerState.UNKNOWN]
744 return bool(
745 self._isValid and
746 self._parts['azimuthSystemState'] not in badStates and
747 self._parts['elevationSystemState'] not in badStates
748 )
750 # Axis inspection properties, designed for internal use. These return
751 # iterables so that they can be used in any() and all() calls, which make
752 # the logic much easier to read, e.g. to see if anything is moving, we can
753 # write `if not any(_axisInMotion):`
754 @property
755 def _axesInFault(self):
756 return [x in self.FAULT_LIKE for x in self.system]
758 @property
759 def _axesOff(self):
760 return [x in self.OFF_LIKE for x in self.system]
762 @property
763 def _axesOn(self):
764 return [not x for x in self._axesOn]
766 @property
767 def _axesInMotion(self):
768 return [x in self.MOVING_LIKE for x in self.motion]
770 @property
771 def _axesTRACKING(self):
772 """Note this is deliberately named _axesTRACKING and not _axesTracking
773 to make it clear that this is the AxisMotionState type of TRACKING and
774 not the normal conceptual notion of tracking (the sky, i.e. as opposed
775 to slewing).
776 """
777 return [x == AxisMotionState.TRACKING for x in self.motion]
779 @property
780 def _axesInPosition(self):
781 return [x is True for x in self.inPosition]
783 @property
784 def state(self):
785 """The overall state of the TMA.
787 Note that this is both a property, and also the method which applies
788 the logic sieve to determine the state at a given point in time.
790 Returns
791 -------
792 state : `lsst.summit.utils.tmaUtils.TMAState`
793 The overall state of the TMA.
794 """
795 # first, check we're valid, and if not, return UNINITIALIZED state, as
796 # things are unknown
797 if not self._isValid:
798 return TMAState.UNINITIALIZED
800 # if we're not in engineering mode, i.e. we're under normal CSC
801 # control, then if anything is in fault, we're in fault. If we're
802 # engineering then some axes will move when others are in fault
803 if not self.engineeringMode:
804 if any(self._axesInFault):
805 return TMAState.FAULT
806 else:
807 # we're in engineering mode, so return fault state if ALL are in
808 # fault
809 if all(self._axesInFault):
810 return TMAState.FAULT
812 # if all axes are off, the TMA is OFF
813 if all(self._axesOff):
814 return TMAState.OFF
816 # we know we're valid and at least some axes are not off, so see if
817 # we're in motion if no axes are moving, we're stopped
818 if not any(self._axesInMotion):
819 return TMAState.STOPPED
821 # now we know we're initialized, and that at least one axis is moving
822 # so check axes for motion and in position. If all axes are tracking
823 # and all are in position, we're tracking the sky
824 if (all(self._axesTRACKING) and all(self._axesInPosition)):
825 return TMAState.TRACKING
827 # we now know explicitly that not everything is in position, so we no
828 # longer need to check that. We do actually know that something is in
829 # motion, but confirm that's the case and return SLEWING
830 if (any(self._axesInMotion)):
831 return TMAState.SLEWING
833 # if we want to differentiate between MOVING_POINT_TO_POINT moves,
834 # JOGGING moves and regular slews, the logic in the step above needs to
835 # be changed and the new steps added here.
837 raise RuntimeError('State error: fell through the state sieve - rewrite your logic!')
840class TMAEventMaker:
841 """A class to create per-dayObs TMAEvents for the TMA's movements.
843 Example usage:
844 >>> dayObs = 20230630
845 >>> eventMaker = TMAEventMaker()
846 >>> events = eventMaker.getEvents(dayObs)
847 >>> print(f'Found {len(events)} for {dayObs=}')
849 Parameters
850 ----------
851 client : `lsst_efd_client.efd_helper.EfdClient`, optional
852 The EFD client to use, created if not provided.
853 """
854 # the topics which need logical combination to determine the overall mount
855 # state. Will need updating as new components are added to the system.
857 # relevant column: 'state'
858 _movingComponents = [
859 'lsst.sal.MTMount.logevent_azimuthMotionState',
860 'lsst.sal.MTMount.logevent_elevationMotionState',
861 ]
863 # relevant column: 'inPosition'
864 _inPositionComponents = [
865 'lsst.sal.MTMount.logevent_azimuthInPosition',
866 'lsst.sal.MTMount.logevent_elevationInPosition',
867 ]
869 # the components which, if in fault, put the TMA into fault
870 # relevant column: 'powerState'
871 _stateComponents = [
872 'lsst.sal.MTMount.logevent_azimuthSystemState',
873 'lsst.sal.MTMount.logevent_elevationSystemState',
874 ]
876 def __init__(self, client=None):
877 if client is not None:
878 self.client = client
879 else:
880 self.client = makeEfdClient()
881 self.log = logging.getLogger(__name__)
882 self._data = {}
884 @dataclass(frozen=True)
885 class ParsedState:
886 eventStart: Time
887 eventEnd: int
888 previousState: TMAState
889 state: TMAState
891 @staticmethod
892 def isToday(dayObs):
893 """Find out if the specified dayObs is today, or in the past.
895 If the day is today, the function returns ``True``, if it is in the
896 past it returns ``False``. If the day is in the future, a
897 ``ValueError`` is raised, as this indicates there is likely an
898 off-by-one type error somewhere in the logic.
900 Parameters
901 ----------
902 dayObs : `int`
903 The dayObs to check, in the format YYYYMMDD.
905 Returns
906 -------
907 isToday : `bool`
908 ``True`` if the dayObs is today, ``False`` if it is in the past.
910 Raises
911 ValueError: if the dayObs is in the future.
912 """
913 todayDayObs = getCurrentDayObs_int()
914 if dayObs == todayDayObs:
915 return True
916 if dayObs > todayDayObs:
917 raise ValueError("dayObs is in the future")
918 return False
920 @staticmethod
921 def _shortName(topic):
922 """Get the short name of a topic.
924 Parameters
925 ----------
926 topic : `str`
927 The topic to get the short name of.
929 Returns
930 -------
931 shortName : `str`
932 The short name of the topic, e.g. 'azimuthInPosition'
933 """
934 # get, for example 'azimuthInPosition' from
935 # lsst.sal.MTMount.logevent_azimuthInPosition
936 return topic.split('_')[-1]
938 def _mergeData(self, data):
939 """Merge a dict of dataframes based on private_efdStamp, recording
940 where each row came from.
942 Given a dict or dataframes, keyed by topic, merge them into a single
943 dataframe, adding a column to record which topic each row came from.
945 Parameters
946 ----------
947 data : `dict` of `str` : `pd.DataFrame`
948 The dataframes to merge.
950 Returns
951 -------
952 merged : `pd.DataFrame`
953 The merged dataframe.
954 """
955 excludeColumns = ['private_efdStamp', 'rowFor']
957 mergeArgs = {
958 'how': 'outer',
959 'sort': True,
960 }
962 merged = None
963 originalRowCounter = 0
965 # Iterate over the keys and merge the corresponding DataFrames
966 for key, df in data.items():
967 if df.empty:
968 # Must skip the df if it's empty, otherwise the merge will fail
969 # due to lack of private_efdStamp. Because other axes might
970 # still be in motion, so we still want to merge what we have
971 continue
973 originalRowCounter += len(df)
974 component = self._shortName(key) # Add suffix to column names to identify the source
975 suffix = '_' + component
977 df['rowFor'] = component
979 columnsToSuffix = [col for col in df.columns if col not in excludeColumns]
980 df_to_suffix = df[columnsToSuffix].add_suffix(suffix)
981 df = pd.concat([df[excludeColumns], df_to_suffix], axis=1)
983 if merged is None:
984 merged = df.copy()
985 else:
986 merged = pd.merge(merged, df, **mergeArgs)
988 merged = merged.loc[:, ~merged.columns.duplicated()] # Remove duplicate columns after merge
990 if len(merged) != originalRowCounter:
991 self.log.warning("Merged data has a different number of rows to the original data, some"
992 " timestamps (rows) will contain more than one piece of actual information.")
994 # if the index is still a DatetimeIndex here then we didn't actually
995 # merge any data, so there is only data from a single component.
996 # This is likely to result in no events, but not necessarily, and for
997 # generality, instead we convert to a range index to ensure consistency
998 # in the returned data, and allow processing to continue.
999 if isinstance(merged.index, pd.DatetimeIndex):
1000 self.log.warning("Data was only found for a single component in the EFD.")
1001 merged.reset_index(drop=True, inplace=True)
1003 return merged
1005 def getEvent(self, dayObs, seqNum):
1006 """Get a specific event for a given dayObs and seqNum.
1008 Repeated calls for the same ``dayObs`` will use the cached data if the
1009 day is in the past, and so will be much quicker. If the ``dayObs`` is
1010 the current day then the EFD will be queried for new data for each
1011 call, so a call which returns ``None`` on the first try might return an
1012 event on the next, if the TMA is still moving and thus generating
1013 events.
1015 Parameters
1016 ----------
1017 dayObs : `int`
1018 The dayObs to get the event for.
1019 seqNum : `int`
1020 The sequence number of the event to get.
1022 Returns
1023 -------
1024 event : `lsst.summit.utils.tmaUtils.TMAEvent`
1025 The event for the specified dayObs and seqNum, or `None` if the
1026 event was not found.
1027 """
1028 events = self.getEvents(dayObs)
1029 if seqNum <= len(events):
1030 event = events[seqNum]
1031 if event.seqNum != seqNum:
1032 # it's zero-indexed and contiguous so this must be true but
1033 # a sanity check doesn't hurt.
1034 raise AssertionError(f"Event sequence number mismatch: {event.seqNum} != {seqNum}")
1035 return event
1036 else:
1037 self.log.warning(f"Event {seqNum} not found for {dayObs}")
1038 return None
1040 def getEvents(self, dayObs):
1041 """Get the TMA events for the specified dayObs.
1043 Gets the required mount data from the cache or the EFD as required,
1044 handling whether we're working with live vs historical data. The
1045 dataframes from the EFD is merged and applied to the TMAStateMachine,
1046 and that series of state changes is used to generate a list of
1047 TmaEvents for the day's data.
1049 If the data is for the current day, i.e. if new events can potentially
1050 land, then if the last event is "open" (meaning that the TMA appears to
1051 be in motion and thus the event is growing with time), then that event
1052 is excluded from the event list as it is expected to be changing with
1053 time, and will likely close eventually. However, if that situation
1054 occurs on a day in the past, then that event can never close, and the
1055 event is therefore included, but a warning about the open event is
1056 logged.
1058 Parameters
1059 ----------
1060 dayObs : `int`
1061 The dayObs for which to get the events.
1063 Returns
1064 -------
1065 events : `list` of `lsst.summit.utils.tmaUtils.TMAState`
1066 The events for the specified dayObs.
1067 """
1068 workingLive = self.isToday(dayObs)
1069 data = None
1071 if workingLive:
1072 # it's potentially updating data, so we must update the date
1073 # regarless of whether we have it already or not
1074 self.log.info(f'Updating mount data for {dayObs} from the EFD')
1075 self._getEfdDataForDayObs(dayObs)
1076 data = self._data[dayObs]
1077 elif dayObs in self._data:
1078 # data is in the cache and it's not being updated, so use it
1079 data = self._data[dayObs]
1080 elif dayObs not in self._data:
1081 # we don't have the data yet, but it's not growing, so put it in
1082 # the cache and use it from there
1083 self.log.info(f'Retrieving mount data for {dayObs} from the EFD')
1084 self._getEfdDataForDayObs(dayObs)
1085 data = self._data[dayObs]
1086 else:
1087 raise RuntimeError("This should never happen")
1089 # if we don't have something to work with, log a warning and return
1090 if not self.dataFound(data):
1091 self.log.warning(f"No EFD data found for {dayObs=}")
1092 return []
1094 # applies the data to the state machine, and generates events from the
1095 # series of states which results
1096 events = self._calculateEventsFromMergedData(data, dayObs, dataIsForCurrentDay=workingLive)
1097 if not events:
1098 self.log.warning(f"Failed to calculate any events for {dayObs=} despite EFD data existing!")
1099 return events
1101 @staticmethod
1102 def dataFound(data):
1103 """Check if any data was found.
1105 Parameters
1106 ----------
1107 data : `pd.DataFrame`
1108 The merged dataframe to check.
1110 Returns
1111 -------
1112 dataFound : `bool`
1113 Whether data was found.
1114 """
1115 # You can't just compare to with data == NO_DATA_SENTINEL because
1116 # `data` is usually a dataframe, and you can't compare a dataframe to a
1117 # string directly.
1118 return not (isinstance(data, str) and data == NO_DATA_SENTINEL)
1120 def _getEfdDataForDayObs(self, dayObs):
1121 """Get the EFD data for the specified dayObs and store it in the cache.
1123 Gets the EFD data for all components, as a dict of dataframes keyed by
1124 component name. These are then merged into a single dataframe in time
1125 order, based on each row's `private_efdStamp`. This is then stored in
1126 self._data[dayObs].
1128 If no data is found, the value is set to ``NO_DATA_SENTINEL`` to
1129 differentiate this from ``None``, as this is what you'd get if you
1130 queried the cache with `self._data.get(dayObs)`. It also marks that we
1131 have already queried this day.
1133 Parameters
1134 ----------
1135 dayObs : `int`
1136 The dayObs to query.
1137 """
1138 data = {}
1139 for component in itertools.chain(
1140 self._movingComponents,
1141 self._inPositionComponents,
1142 self._stateComponents
1143 ):
1144 data[component] = getEfdData(self.client, component, dayObs=dayObs, warn=False)
1145 self.log.debug(f"Found {len(data[component])} for {component}")
1147 if all(dataframe.empty for dataframe in data.values()):
1148 # if every single dataframe is empty, set the sentinel and don't
1149 # try to merge anything, otherwise merge all the data we found
1150 self.log.debug(f"No data found for {dayObs=}")
1151 # a sentinel value that's not None
1152 self._data[dayObs] = NO_DATA_SENTINEL
1153 else:
1154 merged = self._mergeData(data)
1155 self._data[dayObs] = merged
1157 def _calculateEventsFromMergedData(self, data, dayObs, dataIsForCurrentDay):
1158 """Calculate the list of events from the merged data.
1160 Runs the merged data, row by row, through the TMA state machine (with
1161 ``tma.apply``) to get the overall TMA state at each row, building a
1162 dict of these states, keyed by row number.
1164 This time-series of TMA states are then looped over (in
1165 `_statesToEventTuples`), building a list of tuples representing the
1166 start and end of each event, the type of the event, and the reason for
1167 the event ending.
1169 This list of tuples is then passed to ``_makeEventsFromStateTuples``,
1170 which actually creates the ``TMAEvent`` objects.
1172 Parameters
1173 ----------
1174 data : `pd.DataFrame`
1175 The merged dataframe to use.
1176 dayObs : `int`
1177 The dayObs for the data.
1178 dataIsForCurrentDay : `bool`
1179 Whether the data is for the current day. Determines whether to
1180 allow an open last event or not.
1182 Returns
1183 -------
1184 events : `list` of `lsst.summit.utils.tmaUtils.TMAEvent`
1185 The events for the specified dayObs.
1186 """
1187 engineeringMode = True
1188 tma = TMAStateMachine(engineeringMode=engineeringMode)
1190 # For now, we assume that the TMA starts each day able to move, but
1191 # stationary. If this turns out to cause problems, we will need to
1192 # change to loading data from the previous day(s), and looking back
1193 # through it in time until a state change has been found for every
1194 # axis. For now though, Bruno et. al think this is acceptable and
1195 # preferable.
1196 _initializeTma(tma)
1198 tmaStates = {}
1199 for rowNum, row in data.iterrows():
1200 tma.apply(row)
1201 tmaStates[rowNum] = tma.state
1203 stateTuples = self._statesToEventTuples(tmaStates, dataIsForCurrentDay)
1204 events = self._makeEventsFromStateTuples(stateTuples, dayObs, data)
1205 self.addBlockDataToEvents(dayObs, events)
1206 return events
1208 def _statesToEventTuples(self, states, dataIsForCurrentDay):
1209 """Get the event-tuples from the dictionary of TMAStates.
1211 Chunks the states into blocks of the same state, so that we can create
1212 an event for each block in `_makeEventsFromStateTuples`. Off-type
1213 states are skipped over, with each event starting when the telescope
1214 next resumes motion or changes to a different type of motion state,
1215 i.e. from non-tracking type movement (MOVE_POINT_TO_POINT, JOGGING,
1216 TRACKING-but-not-in-position, i.e. slewing) to a tracking type
1217 movement, or vice versa.
1219 Parameters
1220 ----------
1221 states : `dict` of `int` : `lsst.summit.utils.tmaUtils.TMAState`
1222 The states of the TMA, keyed by row number.
1223 dataIsForCurrentDay : `bool`
1224 Whether the data is for the current day. Determines whether to
1225 allow and open last event or not.
1227 Returns
1228 -------
1229 parsedStates : `list` of `tuple`
1230 The parsed states, as a list of tuples of the form:
1231 ``(eventStart, eventEnd, eventType, endReason)``
1232 """
1233 # Consider rewriting this with states as a list and using pop(0)?
1234 skipStates = (TMAState.STOPPED, TMAState.OFF, TMAState.FAULT)
1236 parsedStates = []
1237 eventStart = None
1238 rowNum = 0
1239 nRows = len(states)
1240 while rowNum < nRows:
1241 previousState = None
1242 state = states[rowNum]
1243 # if we're not in an event, fast forward through off-like rows
1244 # until a new event starts
1245 if eventStart is None and state in skipStates:
1246 rowNum += 1
1247 continue
1249 # we've started a new event, so walk through it and find the end
1250 eventStart = rowNum
1251 previousState = state
1252 rowNum += 1 # move to the next row before starting the while loop
1253 if rowNum == nRows:
1254 # we've reached the end of the data, and we're still in an
1255 # event, so don't return this presumably in-progress event
1256 self.log.warning('Reached the end of the data while starting a new event')
1257 break
1258 state = states[rowNum]
1259 while state == previousState:
1260 rowNum += 1
1261 if rowNum == nRows:
1262 break
1263 state = states[rowNum]
1264 parsedStates.append(
1265 self.ParsedState(
1266 eventStart=eventStart,
1267 eventEnd=rowNum,
1268 previousState=previousState,
1269 state=state
1270 )
1271 )
1272 if state in skipStates:
1273 eventStart = None
1275 # done parsing, just check the last event is valid
1276 if parsedStates: # ensure we have at least one event
1277 lastEvent = parsedStates[-1]
1278 if lastEvent.eventEnd == nRows:
1279 # Generally, you *want* the timespan for an event to be the
1280 # first row of the next event, because you were in that state
1281 # right up until that state change. However, if that event is
1282 # a) the last one of the day and b) runs right up until the end
1283 # of the dataframe, then there isn't another row, so this will
1284 # overrun the array.
1285 #
1286 # If the data is for the current day then this isn't a worry,
1287 # as we're likely still taking data, and this event will likely
1288 # close yet, so we don't issue a warning, and simply drop the
1289 # event from the list.
1291 # However, if the data is for a past day then no new data will
1292 # come to close the event, so allow the event to be "open", and
1293 # issue a warning
1294 if dataIsForCurrentDay:
1295 self.log.info("Discarding open (likely in-progess) final event from current day's events")
1296 parsedStates = parsedStates[:-1]
1297 else:
1298 self.log.warning("Last event ends open, forcing it to end at end of the day's data")
1299 # it's a tuple, so (deliberately) awkward to modify
1300 parsedStates[-1] = self.ParsedState(
1301 eventStart=lastEvent.eventStart,
1302 eventEnd=lastEvent.eventEnd - 1,
1303 previousState=lastEvent.previousState,
1304 state=lastEvent.state
1305 )
1307 return parsedStates
1309 def addBlockDataToEvents(self, dayObs, events):
1310 """Find all the block data in the EFD for the specified events.
1312 Finds all the block data in the EFD relating to the events, parses it,
1313 from the rows of the dataframe, and adds it to the events in place.
1315 Parameters
1316 ----------
1317 events : `lsst.summit.utils.tmaUtils.TMAEvent` or
1318 `list` of `lsst.summit.utils.tmaUtils.TMAEvent`
1319 One or more events to get the block data for.
1320 """
1321 try:
1322 blockParser = BlockParser(dayObs, client=self.client)
1323 except Exception as e:
1324 # adding the block data should never cause a failure so if we can't
1325 # get the block data, log a warning and return. It is, however,
1326 # never expected, so use log.exception to get the full traceback
1327 # and scare users so it gets reported
1328 self.log.exception(f'Failed to parse block data for {dayObs=}, {e}')
1329 return
1330 blocks = blockParser.getBlockNums()
1331 blockDict = {}
1332 for block in blocks:
1333 blockDict[block] = blockParser.getSeqNums(block)
1335 for block, seqNums in blockDict.items():
1336 for seqNum in seqNums:
1337 blockInfo = blockParser.getBlockInfo(block=block, seqNum=seqNum)
1339 relatedEvents = blockParser.getEventsForBlock(events, block=block, seqNum=seqNum)
1340 for event in relatedEvents:
1341 toSet = [blockInfo]
1342 if event.blockInfos is not None:
1343 existingInfo = event.blockInfos
1344 existingInfo.append(blockInfo)
1345 toSet = existingInfo
1347 # Add the blockInfo to the TMAEvent. Because this is a
1348 # frozen dataclass, use object.__setattr__ to set the
1349 # attribute. This is the correct way to set a frozen
1350 # dataclass attribute after creation.
1351 object.__setattr__(event, 'blockInfos', toSet)
1353 def _makeEventsFromStateTuples(self, states, dayObs, data):
1354 """For the list of state-tuples, create a list of ``TMAEvent`` objects.
1356 Given the underlying data, and the start/stop points for each event,
1357 create the TMAEvent objects for the dayObs.
1359 Parameters
1360 ----------
1361 states : `list` of `tuple`
1362 The parsed states, as a list of tuples of the form:
1363 ``(eventStart, eventEnd, eventType, endReason)``
1364 dayObs : `int`
1365 The dayObs for the data.
1366 data : `pd.DataFrame`
1367 The merged dataframe.
1369 Returns
1370 -------
1371 events : `list` of `lsst.summit.utils.tmaUtils.TMAEvent`
1372 The events for the specified dayObs.
1373 """
1374 seqNum = 0
1375 events = []
1376 for parsedState in states:
1377 begin = data.iloc[parsedState.eventStart]['private_efdStamp']
1378 end = data.iloc[parsedState.eventEnd]['private_efdStamp']
1379 beginAstropy = efdTimestampToAstropy(begin)
1380 endAstropy = efdTimestampToAstropy(end)
1381 duration = end - begin
1382 event = TMAEvent(
1383 dayObs=dayObs,
1384 seqNum=seqNum,
1385 type=parsedState.previousState,
1386 endReason=parsedState.state,
1387 duration=duration,
1388 begin=beginAstropy,
1389 end=endAstropy,
1390 blockInfos=None, # this is added later
1391 _startRow=parsedState.eventStart,
1392 _endRow=parsedState.eventEnd,
1393 )
1394 events.append(event)
1395 seqNum += 1
1396 return events
1398 @staticmethod
1399 def printTmaDetailedState(tma):
1400 """Print the full state of all the components of the TMA.
1402 Currently this is the azimuth and elevation axes' power and motion
1403 states, and their respective inPosition statuses.
1405 Parameters
1406 ----------
1407 tma : `lsst.summit.utils.tmaUtils.TMAStateMachine`
1408 The TMA state machine in the state we want to print.
1409 """
1410 axes = ['azimuth', 'elevation']
1411 p = tma._parts
1412 axisPad = len(max(axes, key=len)) # length of the longest axis string == 9 here, but this is general
1413 motionPad = max(len(s.name) for s in AxisMotionState)
1414 powerPad = max(len(s.name) for s in PowerState)
1416 # example output to show what's being done with the padding:
1417 # azimuth - Power: ON Motion: STOPPED InPosition: True # noqa: W505
1418 # elevation - Power: ON Motion: MOVING_POINT_TO_POINT InPosition: False # noqa: W505
1419 for axis in axes:
1420 print(f"{axis:>{axisPad}} - "
1421 f"Power: {p[f'{axis}SystemState'].name:>{powerPad}} "
1422 f"Motion: {p[f'{axis}MotionState'].name:>{motionPad}} "
1423 f"InPosition: {p[f'{axis}InPosition']}")
1424 print(f"Overall system state: {tma.state.name}")
1426 def printFullDayStateEvolution(self, dayObs, taiOrUtc='utc'):
1427 """Print the full TMA state evolution for the specified dayObs.
1429 Replays all the data from the EFD for the specified dayObs through
1430 the TMA state machine, and prints both the overall and detailed state
1431 of the TMA for each row.
1433 Parameters
1434 ----------
1435 dayObs : `int`
1436 The dayObs for which to print the state evolution.
1437 taiOrUtc : `str`, optional
1438 Whether to print the timestamps in TAI or UTC. Default is UTC.
1439 """
1440 # create a fake event which spans the whole day, and then use
1441 # printEventDetails code while skipping the header to print the
1442 # evolution.
1443 _ = self.getEvents(dayObs) # ensure the data has been retrieved from the EFD
1444 data = self._data[dayObs]
1445 lastRowNum = len(data) - 1
1447 fakeEvent = TMAEvent(
1448 dayObs=dayObs,
1449 seqNum=-1, # anything will do
1450 type=TMAState.OFF, # anything will do
1451 endReason=TMAState.OFF, # anything will do
1452 duration=-1, # anything will do
1453 begin=efdTimestampToAstropy(data.iloc[0]['private_efdStamp']),
1454 end=efdTimestampToAstropy(data.iloc[-1]['private_efdStamp']),
1455 _startRow=0,
1456 _endRow=lastRowNum
1457 )
1458 self.printEventDetails(fakeEvent, taiOrUtc=taiOrUtc, printHeader=False)
1460 def printEventDetails(self, event, taiOrUtc='tai', printHeader=True):
1461 """Print a detailed breakdown of all state transitions during an event.
1463 Note: this is not the most efficient way to do this, but it is much the
1464 cleanest with respect to the actual state machine application and event
1465 generation code, and is easily fast enough for the cases it will be
1466 used for. It is not worth complicating the normal state machine logic
1467 to try to use this code.
1469 Parameters
1470 ----------
1471 event : `lsst.summit.utils.tmaUtils.TMAEvent`
1472 The event to display the details of.
1473 taiOrUtc : `str`, optional
1474 Whether to display time strings in TAI or UTC. Defaults to TAI.
1475 Case insensitive.
1476 printHeader : `bool`, optional
1477 Whether to print the event summary. Defaults to True. The primary
1478 reason for the existence of this option is so that this same
1479 printing function can be used to show the evolution of a whole day
1480 by supplying a fake event which spans the whole day, but this event
1481 necessarily has a meaningless summary, and so needs suppressing.
1482 """
1483 taiOrUtc = taiOrUtc.lower()
1484 if taiOrUtc not in ['tai', 'utc']:
1485 raise ValueError(f'Got unsuppoted value for {taiOrUtc=}')
1486 useUtc = taiOrUtc == 'utc'
1488 if printHeader:
1489 print(f"Details for {event.duration:.2f}s {event.type.name} event dayObs={event.dayObs}"
1490 f" seqNum={event.seqNum}:")
1491 print(f"- Event began at: {event.begin.utc.isot if useUtc else event.begin.isot}")
1492 print(f"- Event ended at: {event.end.utc.isot if useUtc else event.end.isot}")
1494 dayObs = event.dayObs
1495 data = self._data[dayObs]
1496 startRow = event._startRow
1497 endRow = event._endRow
1498 nRowsToApply = endRow - startRow + 1
1499 print(f"\nTotal number of rows in the merged dataframe: {len(data)}")
1500 if printHeader:
1501 print(f"of which rows {startRow} to {endRow} (inclusive) relate to this event.")
1503 # reconstruct all the states
1504 tma = TMAStateMachine(engineeringMode=True)
1505 _initializeTma(tma)
1507 tmaStates = {}
1508 firstAppliedRow = True # flag to print a header on the first row that's applied
1509 for rowNum, row in data.iterrows(): # must replay rows right from start to get full correct state
1510 if rowNum == startRow:
1511 # we've not yet applied this row, so this is the state just
1512 # before event
1513 print(f"\nBefore the event the TMA was in state {tma.state.name}:")
1514 self.printTmaDetailedState(tma)
1516 if rowNum >= startRow and rowNum <= endRow:
1517 if firstAppliedRow: # only print this intro on the first row we're applying
1518 print(f"\nThen, applying the {nRowsToApply} rows of data for this event, the state"
1519 " evolved as follows:\n")
1520 firstAppliedRow = False
1522 # break the row down and print its details
1523 rowFor = row['rowFor']
1524 axis, rowType = getAxisAndType(rowFor) # e.g. elevation, MotionState
1525 value = tma._getRowPayload(row, rowType, rowFor)
1526 valueStr = f"{str(value) if isinstance(value, bool) else value.name}"
1527 rowTime = efdTimestampToAstropy(row['private_efdStamp'])
1528 print(f"On row {rowNum} the {axis} axis had the {rowType} set to {valueStr} at"
1529 f" {rowTime.utc.isot if useUtc else rowTime.isot}")
1531 # then apply it as usual, printing the state right afterwards
1532 tma.apply(row)
1533 tmaStates[rowNum] = tma.state
1534 self.printTmaDetailedState(tma)
1535 print()
1537 else:
1538 # if it's not in the range of interest then just apply it
1539 # silently as usual
1540 tma.apply(row)
1541 tmaStates[rowNum] = tma.state
1543 def findEvent(self, time):
1544 """Find the event which contains the specified time.
1546 If the specified time lies within an event, that event is returned. If
1547 it is at the exact start, that is logged, and if that start point is
1548 shared by the end of the previous event, that is logged too. If the
1549 event lies between events, the events either side are logged, but
1550 ``None`` is returned. If the time lies before the first event of the
1551 day a warning is logged, as for times after the last event of the day.
1553 Parameters
1554 ----------
1555 time : `astropy.time.Time`
1556 The time.
1558 Returns
1559 -------
1560 event : `lsst.summit.utils.tmaUtils.TMAEvent` or `None`
1561 The event which contains the specified time, or ``None`` if the
1562 time doesn't fall during an event.
1563 """
1564 # there are five possible cases:
1565 # 1) the time lies before the first event of the day
1566 # 2) the time lies after the last event of the day
1567 # 3) the time lies within an event
1568 # 3a) the time is exactly at the start of an event
1569 # 3b) if so, time can be shared by the end of the previous event if
1570 # they are contiguous
1571 # 4) the time lies between two events
1572 # 5) the time is exactly at end of the last event of the day. This is
1573 # an issue because event end times are exclusive, so this time is
1574 # not technically in that event, it's the moment it closes (and if
1575 # there *was* an event which followed contiguously, it would be in
1576 # that event instead, which is what motivates this definition of
1577 # lies within what event)
1579 dayObs = getDayObsForTime(time)
1580 # we know this is on the right day, and definitely before the specified
1581 # time, but sanity check this before continuing as this needs to be
1582 # true for this to give the correct answer
1583 assert getDayObsStartTime(dayObs) <= time
1584 assert getDayObsEndTime(dayObs) > time
1586 # command start to many log messages so define once here
1587 logStart = f"Specified time {time.isot} falls on {dayObs=}"
1589 events = self.getEvents(dayObs)
1590 if len(events) == 0:
1591 self.log.warning(f'There are no events found for {dayObs}')
1592 return None
1594 # check case 1)
1595 if time < events[0].begin:
1596 self.log.warning(f'{logStart} and is before the first event of the day')
1597 return None
1599 # check case 2)
1600 if time > events[-1].end:
1601 self.log.warning(f'{logStart} and is after the last event of the day')
1602 return None
1604 # check case 5)
1605 if time == events[-1].end:
1606 self.log.warning(f'{logStart} and is exactly at the end of the last event of the day'
1607 f' (seqnum={events[-1].seqNum}). Because event intervals are half-open, this'
1608 ' time does not technically lie in any event')
1609 return None
1611 # we are now either in an event, or between events. Walk through the
1612 # events, and if the end of the event is after the specified time, then
1613 # we're either in it or past it, so check if we're in.
1614 for eventNum, event in enumerate(events):
1615 if event.end > time: # case 3) we are now into or past the right event
1616 # the event end encloses the time, so note the > and not >=,
1617 # this must be strictly greater, we check the overlap case
1618 # later
1619 if time >= event.begin: # we're fully inside the event, so return it.
1620 # 3a) before returning, check if we're exactly at the start
1621 # of the event, and if so, log it. Then 3b) also check if
1622 # we're at the exact end of the previous event, and if so,
1623 # log that too.
1624 if time == event.begin:
1625 self.log.info(f"{logStart} and is exactly at the start of event"
1626 f" {eventNum}")
1627 if eventNum == 0: # I think this is actually impossible, but check anyway
1628 return event # can't check the previous event so return here
1629 previousEvent = events[eventNum - 1]
1630 if previousEvent.end == time:
1631 self.log.info("Previous event is contiguous, so this time is also at the exact"
1632 f" end of {eventNum - 1}")
1633 return event
1634 else: # case 4)
1635 # the event end is past the time, but it's not inside the
1636 # event, so we're between events. Log which we're between
1637 # and return None
1638 previousEvent = events[eventNum - 1]
1639 timeAfterPrev = (time - previousEvent.end).to_datetime()
1640 naturalTimeAfterPrev = humanize.naturaldelta(timeAfterPrev, minimum_unit='MICROSECONDS')
1641 timeBeforeCurrent = (event.begin - time).to_datetime()
1642 naturalTimeBeforeCurrent = humanize.naturaldelta(timeBeforeCurrent,
1643 minimum_unit='MICROSECONDS')
1644 self.log.info(f"{logStart} and lies"
1645 f" {naturalTimeAfterPrev} after the end of event {previousEvent.seqNum}"
1646 f" and {naturalTimeBeforeCurrent} before the start of event {event.seqNum}."
1647 )
1648 return None
1650 raise RuntimeError('Event finding logic fundamentally failed, which should never happen - the code'
1651 ' needs fixing')