Pseudo-MSIDs in the engineering archive¶

A small selection of pseudo-MSIDs that do not come in the engineering telemetry stream are also available in the archive. These are:

ACIS DEA housekeeping: status from the DEA (including detector focal plane temperature)
Ephemeris: predictive and definitive orbital (Chandra), solar, and lunar ephemeris values
SIM telemetry: SIM position and moving status
Derived parameters: values computed from other MSIDs in the archive and stored in archive files
Computed parameters: values computed on the fly from other MSIDs

ACIS DEA housekeeping¶

The ACIS DEA telemeters a variety of useful information that is sent in an event-based format via queries to the processor. The engineering archive reformats those telemetry queries (one per psuedo-MSID) into records that match the engineering archive format where all queries with the same time stamp are place in a single record.

The time sample of these data vary but are typically around once per 16 seconds. Because of what appears to be an issue with CXCDS decom there are frequently bad values at the beginning of an archive file. For this reason it is especially important to perform a fetch with the filter_bad=True setting.

MSID	Unit	Description
tmp_bep_pcb	K	DPA Thermistor 1 - BEP PC Board
tmp_bep_osc	K	DPA Thermistor 2 - BEP Oscillator
tmp_fep0_mong	K	DPA Thermistor 3 - FEP 0 Mongoose
tmp_fep0_pcb	K	DPA Thermistor 4 - FEP 0 PC Board
tmp_fep0_actel	K	DPA Thermistor 5 - FEP 0 ACTEL
tmp_fep0_ram	K	DPA Thermistor 6 - FEP 0 RAM
tmp_fep0_fb	K	DPA Thermistor 7 - FEP 0 Frame Buf
tmp_fep1_mong	K	DPA Thermistor 8 - FEP 1 Mongoose
tmp_fep1_pcb	K	DPA Thermistor 9 - FEP 1 PC Board
tmp_fep1_actel	K	DPA Thermistor 10 - FEP 1 ACTEL
tmp_fep1_ram	K	DPA Thermistor 11 - FEP 1 RAM
tmp_fep1_fb	K	DPA Thermistor 12 - FEP 1 Frame Buf
fptemp_12	K	Focal Plane Temp. Board 12
fptemp_11	K	Focal Plane Temp. Board 11
dpagndref1	V	DPA Ground Reference 1
dpa5vhka	V	DPA 5V Housekeeping A
dpagndref2	V	DPA Ground Reference 2
dpa5vhkb	V	DPA 5V Housekeeping B
dea28volta	V	Primary Raw DEA 28V DC
dea24volta	V	Primary Raw DEA 24V DC
deam15volta	V	Primary Raw DEA -15.5V
deap15volta	V	Primary Raw DEA +15.5V
deam6volta	V	Primary Raw DEA -6V DC
deap6volta	V	Primary Raw DEA +6V DC
rad_pcb_a		Relative Dose Rad. Monitor Side A
gnd_1	V	Interface Ground Reference
dea28voltb	V	Backup Raw DEA 28V DC
dea24voltb	V	Backup DEA 24V DC
deam15voltb	V	Backup DEA -15.5V DC
deap15voltb	V	Backup DEA +15.5V DC
deam6voltb	V	Backup DEA -6V DC
deap6voltb	V	Backup DEA +6V DC
rad_pcb_b		Relative Dose Rad. Monitor Side B
gnd_2	V	Ground

Ephemeris¶

CXC processing generates definitive and predictive ephemeris files for the Chandra, the Moon, and the Sun. Values are given with respect to Earth (ECI). Predictive values are available into the near future while definitive values will be a few weeks behind the present. (Note that daily and 5-minute statistics are only available up to the present time). These values are contained within the following content types:

Content	Object	Type
orbitephem0	Chandra	Predictive
lunarephem0	Moon	Predictive
solarephem0	Sun	Predictive
orbitephem1	Chandra	Definitive
lunarephem1	Moon	Definitive
solarephem1	Sun	Definitive

The psuedo-MSIDs for each of the ephemeris elements is given in the following table, where <CONTENT> is replaced by the appropriate Content value from the previous table.

MSID	Unit	Description
<CONTENT>_x	m	X position (ECI)
<CONTENT>_y	m	Y position (ECI)
<CONTENT>_z	m	Z position (ECI)
<CONTENT>_vx	m/s	X velocity (ECI)
<CONTENT>_vy	m/s	Y velocity (ECI)
<CONTENT>_vz	m/s	Z velocity (ECI)

In addition there is a set of pseudo-MSIDs that provide a number of higher-level definitive angle and distance values that are useful.

MSID	Unit	Description
Point_X		Unit Pointing (X)
Point_Y		Unit Pointing (Y)
Point_Z		Unit Pointing (Z)
Point_SunCentAng	deg	Pointing-Solar angle (from center)
Point_SunLimbAng	deg	Pointing-Solar angle (from limb)
Point_MoonCentAng	deg	Pointing-Lunar angle (from center)
Point_MoonLimbAng	deg	Pointing-Lunar angle (from limb)
Point_EarthCentAng	deg	Pointing-Earth angle (from center)
Point_EarthLimbAng	deg	Pointing-Earth angle (from limb)
Dist_SatEarth	m	Sat-Earth distance (from Earth center)
Sun_EarthCentAng	deg	Sun-Earth angle (from center)
Sun_EarthLimbAng	deg	Sun-Earth angle (from limb)
Point_RamVectorAng	deg	Pointing-Ram angle

HRC Secondary Science and Housekeeping¶

The HRC telemeters a variety of useful information that is sent down in secondary science and housekeeping formats (SS and HK hereafter). The engineering archive reformats those telemetry values to be consistent with the MSFC-1949 specification of HRC SS and HK telemetry. It also facilitates handling invalid data in SS and HK telemetry.

Invalid data¶

Invalid HRC SS and HK telemetry can arise in three ways:

When telemetry format changes there is commanding to change the timing signals used to fill the housekeeping and secondary science rates that can result in invalid data being put in these MSIDs for up to a major frame.
When detectors change or a detector is set to its default configuration the FIFO used to hold the housekeeping an secondary science data gets reset which may result in a single bad sample of data.
The secondary-science byte-shift anomaly causes the occasional portion of the housekeeping and sometimes the rate data to be corrupted.

In the Ska archive the presence of these conditions is tracked in a new pseudo-MSID called HRC_SS_HK_BAD.

The first two of these are detected by looking at “spare” bits in the MSID SCIDPREN (i.e. good data satisfies SCIDPREN=0000xxxx000xxxxx. The least-significant 7 bits of HRC_SS_HK_BAD contain a copy of the 7 bits in SCIDPREN which must be 0 for good data. The following code illustrates detecting conditions (1) or (2):

>>> from cheta import fetch
>>> from Chandra.Time import DateTime
>>> dat = fetch.Msid('HRC_SS_HK_BAD', '1999:300', '1999:310')
>>> bad = (dat.vals & 0x7f) > 0
>>> DateTime(dat.times[bad]).date
array(['1999:301:16:10:13.375', '1999:301:16:10:15.425',
       '1999:301:18:16:42.476', '1999:301:18:16:44.526',
       '1999:301:19:20:03.176', '1999:301:19:20:05.226',
       '1999:301:21:28:33.226', '1999:301:21:28:35.276',
       '1999:303:07:13:16.230', '1999:303:07:13:18.280'],
      dtype='|S21')

The third condition is detected by its impact on the MSID 2SMTRATM. If it is less than -20degC or greater than 50degC then the analog housekeeping from the row is marked with bad quality. In this case the HRC_SS_HK_BAD MSID has bit 10 set, which can be detected by a logical-and with 0x0400 (1024).

Querying data¶

For HK telemetry it is sufficient to query the archive using the standard fetch.Msid method which automatically removes bad quality data. This applies for 5-minute and daily stat data as well. For instance:

>>> dat = fetch.Msid('2S2ONST', '2002:200', '2002:250')
>>> dat.plot()

(Source code, png, hires.png, pdf)

For SS the situation is a little different because those do not have the bad quality flags set at the time of data ingest (because the indicators are all in HK). In this case use the fetch.HrcSsMsid method to get a filtered version of the SS MSIDs (2TLEV1RT 2VLEV1RT 2SHEV1RT 2TLEV2RT 2VLEV2RT 2SHEV2RT). For instance to get 5-minute telemetry for 2SHEV1RT use:

>>> dat = fetch.HrcSsMsid('2SHEV1RT', '2002:200', '2002:250', stat='5min')
>>> dat.plot()

(Source code, png, hires.png, pdf)

HK MSIDs¶

The list of available HK MSIDs is:

MSID	Description
224PCAST	+24V LVPS ON/OFF
215PCAST	+15V LVPS ON/OFF
215NCAST	-15V LVPS ON/OFF
2SPTPAST	SPECTROSCOPY DET TOP PLATE HV STEP
2SPBPAST	SPECTROSCOPY DET BOTTOM PLATE HV STEP
2IMTPAST	IMAGING DET TOP PLATE HV STEP
2IMBPAST	IMAGING DET BOTTOM PLATE HV STEP
2NYMTAST	-Y SHUTTER MOTOR SELECTED
2PYMTAST	+Y SHUTTER MOTOR SELECTED
2CLMTAST	CALSRC MOTOR SELECTED
2DRMTAST	DOOR MOTOR SELECTED
2ALMTAST	ALL MOTORS DESELECTED
2MSMDARS	MOTION CONTROL MODE RESET – 2MSMDARS
2MDIRAST	MOTOR DIRECTION
2MSNBAMD	MOTOR STATUS REGISTER MV NSTEPS TOWARD B
2MSNAAMD	MOTOR STATUS REGISTER MV NSTEPS TOWARD A
2MSLBAMD	MOTOR STATUS REGISTER MOVE TO LIMIT SWITCH B
2MSLAAMD	MOTOR STATUS REGISTER MOVE TO LIMIT SWITCH A
2MSPRAMD	MOTOR STATUS REGISTER MOVE TO POSITION R
2MSDRAMD	MOTOR DRIVE ENABLE
2MCMDARS	MOTION CONTROL MODE RESET – 2MCMDARS
2MCNBAMD	MOTOR CMD REGISTER MV NSTEPS TOWARD B
2MCNAAMD	MOTOR CMD REGISTER MV NSTEPS TOWARD A
2MCLBAMD	MOTOR CMD REGISTER MOVE TO LIMIT SWITCH B
2MCLAAMD	MOTOR CMD REGISTER MOVE TO LIMIT SWITCH A
2MCPRAMD	MOTOR COMMAND REGISTER MOVE TO POSITION REGISTER
2MDRVAST	MOTOR CMD REGISTER MOTOR DRIVE ENABLE
2SCTHAST	STEP CTR LAST VALUE
2SMOIAST	SELECTED MOTOR OVERCURRENT FLAG
2SMOTAST	SELECTED MOTOR OVERTEMPERATURE FLAG
2DROTAST	DRV OVERTEMP ENABLE
2DROIAST	DRV OVERCURRENT ENABLE
2SFLGAST	STOP FLAG ENABLE
2OSLSAST	OPEN SECONDARY LIMIT SWITCH ENABLE
2OPLSAST	OPEN PRIMARY LIMIT SWITCH ENABLE
2CSLSAST	CLOS SECONDARY LIMIT SWITCH ENABLE
2CPLSAST	CLOS PRIMARY LIMIT SWITCH ENABLE
2OSLSADT	OPEN SECONDARY LS DETECTED
2OSLSAAC	OPEN SECONDARY LS ACTIVE
2OPLSAAC	OPEN PRIMARY LS ACTIVE
2CSLSADT	CLOS SECONDARY LS DETECTED
2CSLSAAC	CLOS SECONDARY LS ACTIVE
2CPLSAAC	CLOS PRIMARY LS ACTIVE
2FCPUAST	FORCED COARSE POSITION U AXIS
2FCPVAST	FORCED COARSE POSITION V AXIS
2CBHUAST	CENTER BLANK HIGH CP U AXIS
2CBLUAST	CENTER BLANK LOW CP U AXIS
2CBHVAST	CENTER BLANK HIGH CP V AXIS
2CBLVAST	CENTER BLANK LOW CP V AXIS
2WDTHAST	WIDTH THRESHOLD SETTING
2CLMDAST	CALIBRATION MODE ON
2FIFOAVR	DATA FIFO ENABLE
2OBNLASL	OBSERVING/NEXT-IN-LINE MODE SELECT
2SPMDASL	SPECT DETECTOR SPECT/IMG MODE SELECT
2EBLKAVR	EDGE BLANK VALIDITY ENABLE
2CBLKAVR	CENTER BLANK VALIDITY ENABLE
2ULDIAVR	UPPER LEVEL DISCR VALIDITY ENABLE
2WDTHAVR	WIDTH DISCR VALIDITY ENABLE
2SHLDAVR	SHIELD DISCR VALIDITY ENABLE
2SPONST	SPECTROSCOPY DETECTOR HVPS ON/OFF
2SPCLST	SPECTROSCOPY DET HVPS CURRENT LIMIT ENAB
2S1ONST	SHIELD A HVPS ON/OFF
2IMONST	IMAGING DETECTOR HVPS ON/OFF
2IMCLST	IMAGING DET HVPS CURRENT LIMIT ENABLE
2S2ONST	SHIELD B HVPS ON/OFF
2S1HVST	SHIELD A HVPS SETTING
2S2HVST	SHIELD B HVPS SETTING
2C05PALV	+5V BUS MONITOR
2C15PALV	+15V BUS MONITOR
2C15NALV	-15V BUS MONITOR
2C24PALV	+24V BUS MONITOR
2IMHVLV	IMAGING LOWER MCP HV MONITOR
2IMHBLV	IMAGING LOWER & UPPER MCP HV MONITOR
2SPHVLV	SPECTROSCOPY LOWER MCP HV MONITOR
2SPHBLV	SPECTROSCOPY UPPER MCP HV MONITOR
2S1HVLV	SHIELD A HV MONITOR
2S2HVLV	SHIELD B HV MONITOR
2PRBSCR	PRIMARY BUS CURRENT
2PRBSVL	PRIMARY BUS VOLTAGE
2ULDIALV	UPPER LEVEL DISCRIMINATOR SETTING
2LLDIALV	TRIGGER LEVEL DISCRIMINATOR MONITOR
2FEPRATM	FE PREAMP CARD TEMPERATURE
2CALPALV	CAL PULSER AMPLITUDE MONITOR
2GRDVALV	GRID BIAS SETTING MONITOR
2RSRFALV	RANGE SWITCH ANALOG SETTING
2SPINATM	SPECTROSCOPY DETECTOR TEMPERATURE (INSIDE)
2IMINATM	IMAGING DETECTOR TEMPERATURE (INSIDE)
2LVPLATM	LVPS PLATE TEMP
2SPHVATM	SPECTROSCOPY DET HVPS TEMPERATURE
2IMHVATM	IMAGING DET HVPS TEMPERATURE
2SMTRATM	SELECTED MOTOR TEMPERATURE
2FE00ATM	FRONT END TEMPERATURE RT2

SS MSIDs¶

The available SS MSIDs are:

MSID	Description
2TLEV1RT	TOTAL EVENT RATE 1
2VLEV1RT	VALID EVENT RATE 1
2SHEV1RT	SHIELD EVENT RATE 1
2TLEV2RT	TOTAL EVENT RATE 2
2VLEV2RT	VALID EVENT RATE 2
2SHEV2RT	SHIELD EVENT RATE 2

Science Instrument Module¶

Information about the SIM is available via the three following pseudo-MSIDs categories.

SEA standard telemetry¶

The units shown below are for the CXC and ENG unit systems, respectively.

MSID	Unit	Description
3FAFLAAT	K [degC]	SEA FA flexure a temp a
3FAFLBAT	K [degC]	SEA FA flexure b temp a
3FAFLCAT	K [degC]	SEA FA flexure c temp a
3FAMOVE		SEA FA in motion flag
3FAMTRAT	K [degC]	SEA-A focus drive motor temp
3FAPOS	mm [step]	SEA FA position
3FAPSAT	K [degC]	SEA-A power supply temp
3FASEAAT	K [degC]	SEA-A box temp
3LDRTMEK		SEA mechanism for last detected reference tab
3LDRTNO		SEA tab number of reference tab last detected
3LDRTPOS	mm [step]	SEA last detected ref tab position
3MRMMXMV	[step]	SEA max pwm level most recent move
3SEAID		SEA identification
3SEAINCM		SEA invalid command group flag
3SEARAMF		SEA ram failure detection flag
3SEAROMF		SEA prom checksum fail flag
3SEARSET		SEA reset flag
3SEATMUP		SEA tlm update flag (toggle w/ea update)
3SFLXAST	K [degC]	SEA flexure a temperature setpoint
3SFLXBST	K [degC]	SEA flexure b temperature setpoint
3SFLXCST	K [degC]	SEA flexure c temperature setpoint
3SHTREN		SEA heater power relay status
3SMOTOC	cnts	SEA motor drive overcurrent counter
3SMOTPEN		SEA motor driver power relay status
3SMOTSEL		SEA motor selection relay status
3SMOTSTL	cnts	SEA motor stall counter
3SPENDC	cnts	SEA pending cmd count
3STAB2EN		SEA tab2 auto position update enab/disa status
3TRMTRAT	K [degC]	SEA a translation drive motor temp
3TSCMOVE		SEA TSC in motion flag
3TSCPOS	mm [step]	SEA TSC position
TLMSTATUS		SEA telemetry status (updated or not updated)

The state codes for these MSIDs (where applicable) are defined by the CXC SIM level-0 decom specification and differ from the values found in the TDB. The engineering archive state codes are:

MSID	Raw=0	Raw=1
3TSCMOVE	F	T
3FAMOVE	F	T
3SEAID	SEA-A	SEA-B
3SEARSET	F	T
3SEAROMF	F	T
3SEAINCM	F	T
3STAB2EN	DISABLE	ENABLE
3SMOTPEN	ENABLE	DISABLE
3SMOTSEL	TSC	FA
3SHTREN	DISABLE	ENABLE
3SEARAMF	F	T

SEA diagnostic telemetry¶

MSID	Unit	Description
3SDSWELF		SEA CSC Exectuting from RAM
3SDPSTKP		SEA Data Stack Ptr
3SDTSEDG		TSC Tab Edge Detection Flags
3SDFAEDG		FA Tab Edge Detection Flags
3SDMAJFP		Major Frame Period Time Measured by SEA
3SDRMOVD		Most Recent Motor Move Destination
3SDTSTSV	V	TSC Tab Position Sensor A/D converter
3SDFATSV	V	FA Tab Position Sensor A/D Converter
3SDAGV	V	Analog Ground A/D Converter Reading
3SDP15V	V	+15V Power Supply A/D Converter Reading
3SDP5V	V	+5V Power Supply A/D Converter Reading
3SDM15V	V	-15V Power Supply A/D Converter Reading
3SDFLXAT	K [degC]	Flexure A Thermistor A/D Converter
3SDFLXBT	K [degC]	Flexure B Thermistor A/D Converter
3SDFLXCT	K [degC]	Flexure C Thermistor A/D Converter
3SDTSMT	K [degC]	TSC Motor Thermistor A/D Converter
3SDFAMT	K [degC]	FA Motor Thermistor A/D Converter
3SDPST	K [degC]	SEA Power Supply Thermistor A/D Converter
3SDBOXT		SEA Box Thermistor A/D Converter
3SDRMFAD		RAM Most Recent detected Fail Address
3SDTSTBW		TSC Most Recent detected Tab Width
3SDFATBW		FA Most Recent detected Tab Width
3SDSYRS		Process Reset Due Synchronization Loss
3SDWMRS		Processor Warm Reset
3SDTSP		TSC Most Recent PWM Histogram
3SDFAP		FA Most Recent PWM Histogram
3SDINCOD		SEA Invalid CommandCode

The state codes for these MSIDs (where applicable) are defined by the CXC SIM level-0 decom specification and differ from the values found in the TDB. The engineering archive state codes are:

MSID	Raw=0	Raw=1
3SDSWELF	F	T
3SDSYRS	F	T
3SDWMRS	F	T

SIMCOOR (CXC high-level values)¶

Note

These pseudo-MSIDs are deprecated in favor of the standard versions such as 3TSCPOS, 3FAPOS, 3TSCMOV, 3TRMTRAT, etc. which are available in the SEA telemetry described above.

MSID	Unit	Description
SEAIDENT		SEA identification
SIM_X	mm	X position (FA)
SIM_Y	mm	Y position (not meaningful)
SIM_Z	mm	Z position (TSC)
SIM_X_MOVED		FA moved
SIM_Z_MOVED		TSC moved

EPHIN¶

Information about the EPHIN instrument is available via the following pseudo-MSIDs:

MSID	Unit	Description
TLMBLKCNT		EIO TLMBLK count
EIOBITCNT		EIO bit counter
HKOPMODE		HK operational Mode
HKRESET		HK reset Flag
HKDOWNLOAD		HK down load flag
HKUPLOAD		HK upload Flag
HKFRAMECNTR		HK internal frame Counter
HKRINGSEGW		HK ring segment auto switching
HKFAILMODEA		HK failure mode detector A
HKFAILMODEB		HK failure mode detector B
HKHVDETG		HK high voltage detector G
HKHVDETAF		HK high voltage detectors A-F
HKANALOGPWR		HK analog power switchs
HKFAILMODEGC		HK failure mode detectors G-C
HKP5V	V	HK +5V rail voltage
HKP27V	V	HK +27V rail voltage
HKP6V	V	HK +6V rail voltage
HKN6V	V	HK -6V rail voltage
HKP5I	mA	HK +5V rail current
HKP27I	mA	HK +27V rail current
HKP6I	mA	HK +6V rail current
HKN6I	mA	HK -6V rail current
HKEBOXTEMP	K	HK EBox temperature (5EHSE300)
HKABIASLEAKI	uA	HK A bias leakage current
HKBBIASLEAKI	uA	HK B bias leakage current
HKCBIASLEAKI	uA	HK C bias leakage current
HKDBIASLEAKI	uA	HK D bias leakage current
HKEBIASLEAKI	uA	HK E bias leakage current
HKFBIASLEAKI	uA	HK F bias leakage current
HKGHV	V	HK G high voltage
SCOPMODE		Sci operational mode
SCSTATUS		Sci status flags
SCFRAMECNTR		Sci internal Frame Counter
SCCONTROL		Sci control flags
SCRINGSEGSW		Sci ring segment auto switching
SCFAILMODEA		Sci failure mode detectors A
SCFAILMODEB		Sci failure mode detectors B
SCHVDETG		Sci high voltage detector G
SCHVDETAF		Sci high voltage detectors A-F
SCANALOGPWR		Sci analog power switches
SCFAILMODEGC		Sci failure mode detectors G-C
SCPHAPRIPTR		Sci PHA Priority pointer
SCG0		Sci single detector counter G0
SCA00		Sci single detector counter A00
SCA01		Sci single detector counter A01
SCA02		Sci single detector counter A02
SCA03		Sci single detector counter A03
SCA04		Sci single detector counter A04
SCA05		Sci single detector counter A05
SCB00		Sci single detector counter B00
SCB01		Sci single detector counter B01
SCB02		Sci single detector counter B02
SCB03		Sci single detector counter B03
SCB04		Sci single detector counter B04
SCB05		Sci single detector counter B05
SCC0		Sci single detector counter C0
SCD0		Sci single detector counter D0
SCE0		Sci single detector counter E0
SCF0		Sci single detector counter F0
SCP4GM		Sci single detector counter P4GM
SCP4GR		Sci single detector counter P4GR
SCP4S		Sci single detector counter P4S
SCP8GM		Sci single detector counter P8GM
SCP8GR		Sci single detector counter P8GR
SCP8S		Sci single detector counter P8S
SCH4GM		Sci single detector counter H4GM
SCH4GR		Sci single detector counter H4GR
SCH4S1		Sci single detector counter H4S1
SCH4S23		Sci single detector counter H4S23
SCH8GM		Sci single detector counter H8GM
SCH8GR		Sci single detector counter H8GR
SCH8S1		Sci single detector counter H8S1
SCH8S23		Sci single detector counter H8S23
SCE150		Sci single detector counter E150
SCE300		Sci single detector counter E300
SCE1300		Sci single detector counter E1300
SCE3000		Sci single detector counter E3000
SCINT		Sci single detector counter INT
SCP25GM		Sci single detector counter P25GM
SCP25GR		Sci single detector counter P25GR
SCP25S		Sci single detector counter P25S
SCP41GM		Sci single detector counter P41GM
SCP41GR		Sci single detector counter P41GR
SCP41S		Sci single detector counter P41S
SCH25GM		Sci single detector counter H25GM
SCH25GR		Sci single detector counter H25GR
SCH25S1		Sci single detector counter H25S1
SCH25S23		Sci single detector counter H25S23
SCH41GM		Sci single detector counter H41GM
SCH41GR		Sci single detector counter H41GR
SCH41S1		Sci single detector counter H41S1
SCH41S23		Sci single detector counter H41S23
SCCT0		Sci single detector counter CT0
SCCT1		Sci single detector counter CT1
SCCT2		Sci single detector counter CT2
SCCT3		Sci single detector counter CT3
SCCT4		Sci single detector counter CT4
SCCT5		Sci single detector counter CT5

Derived Parameters or Calcs¶

The engineering archive has pseudo-MSIDs that are derived via computation from telemetry MSIDs. These are also known as “calcs” in the context of MAUDE (which inherited this from GRETA). In MAUDE, a calc is normally indicated with a prefix of CALC_, but for compatibility with cheta a prefix of DP_ is also allowed.

Derived parameter names begin with the characters DP_ (not case sensitive as usual). Otherwise there is no difference from standard MSIDs. When querying the archive using fetch, there are three equivalent ways to specify an MSID name:

DP_<name>> e.g. DP_PITCH_FSS
CALC_<name> e.g. CALC_PITCH_FSS
<name> e.g. PITCH_FSS: this is a convenience and internally fetch will search for derived parameters matching DP_<name>.

Available MSIDs¶

To see the available derived parameters or calcs in the CXC archive or MAUDE archive, issue the following commands respectively:

>>> from cheta import fetch
>>> sorted([msid for msid in fetch.data_source.get_msids('cxc')
...        if msid.startswith('DP_')])
>>> sorted([msid for msid in fetch.data_source.get_msids('maude')
...        if msid.startswith('CALC_')])

Definition¶

Derived parameters are defined by inheriting from the DerivedParameter base class. Each class definition requires three class attributes: content_root, rootparams, and time_step. The time_step should be an integral multiple of 0.25625. In the example below a large number of definition classes have the same content root so another class DerivedParameterThermal has been created to avoid repeating the content_root definition every time.

Each definition class also requires a calc(self, data) method. The data argument will be an MSIDset (dict of fetch MSID objects) with values for each of the rootparams MSIDs. The data values in the MSIDset will be filtered for bad values and aligned to a common time sequence with step size time_step.

class DerivedParameterThermal(base.DerivedParameter):
    content_root = 'thermal'

class DP_EE_DIAM(DerivedParameterThermal):
    """Kodak diametrical encircled energy"""
    rootparams = ['OHRMGRD6', 'OHRMGRD3']
    time_step = 32.8

    def calc(self, data):
        VAL2 = np.abs(1.0 * data['OHRMGRD6'].vals)
        VAL1 = np.abs(1.0 * data['OHRMGRD3'].vals)
        DTDIAM = np.max([VAL1, VAL2], axis=0)
        EE_DIAM = DTDIAM * 0.401
        return EE_DIAM

class DP_P01(DerivedParameterThermal):
    """Zone 1 heater power"""
    rootparams = ['ELBV', '4OHTRZ01']
    time_step = 0.25625

    def calc(self, data):
        VSQUARED = data['ELBV'].vals * data['ELBV'].vals
        P01 = data['4OHTRZ01'].vals * VSQUARED / 110.2
        return P01

class DP_DPA_POWER(base.DerivedParameter):
    """ACIS total DPA-A and DPA-B power"""
    rootparams = ['1dp28avo', '1dpicacu', '1dp28bvo', '1dpicbcu']
    time_step = 32.8
    content_root = 'acispow'

    def calc(self, data):
        power = (data['1dp28avo'].vals * data['1dpicacu'].vals +
                 data['1dp28bvo'].vals * data['1dpicbcu'].vals)
        return power

ACIS Power¶

Derived parameter MSIDs related to ACIS power.

class cheta.derived.acispow.DP_DEA_POWER[source]¶: ACIS DEA power

class cheta.derived.acispow.DP_DPA_POWER[source]¶: ACIS total DPA-A and DPA-B power

class cheta.derived.acispow.DP_PSMC_POWER[source]¶: ACIS PSMC power

EPS¶

Derived parameter MSIDs related to EPS subsystem.

Author: B. Bissell

Revision History:

Jul 2014 Initial Version

class cheta.derived.eps.DP_BATT1_TAVE[source]¶

Battery 1 Average Temperature. Derived from average of all three battery temperature sensors. Telemetry 16x / MF

calc(data)[source]¶

rootparams = ['TB1T1', 'TB1T2', 'TB1T3']¶

time_step = 2.05¶

class cheta.derived.eps.DP_BATT2_TAVE[source]¶

Battery 2 Average Temperature. Derived from average of all three battery temperature sensors. Telemetry 16x / MF

calc(data)[source]¶

rootparams = ['TB2T1', 'TB2T2', 'TB2T3']¶

time_step = 2.05¶

class cheta.derived.eps.DP_BATT3_TAVE[source]¶

Battery 3 Average Temperature. Derived from average of all three battery temperature sensors. Telemetry 16x / MF

calc(data)[source]¶

rootparams = ['TB3T1', 'TB3T2', 'TB3T3']¶

time_step = 2.05¶

class cheta.derived.eps.DP_EPOWER1[source]¶

Bus Power = ELBI_LOW * ELBV Telemetry 8x / MF

calc(data)[source]¶

rootparams = ['ELBI_LOW', 'ELBV']¶

time_step = 4.1¶

class cheta.derived.eps.DP_MYSAPOW[source]¶

-Y Solar Array Power = ESAMYI * ELBV Telemetry 8x / MF

calc(data)[source]¶

rootparams = ['ESAMYI', 'ELBV']¶

time_step = 4.1¶

class cheta.derived.eps.DP_PYSAPOW[source]¶

+Y Solar Array Power = ESAPYI * ELBV Telemetry 8x / MF

calc(data)[source]¶

rootparams = ['ESAPYI', 'ELBV']¶

time_step = 4.1¶

class cheta.derived.eps.DerivedParameterEps[source]¶

content_root = 'eps'¶

Orbital elements¶

class cheta.derived.orbit.DP_APOGEE_RADIUS[source]¶

Orbit apogee based on orbital elements (m)

Defined as semi_major_axis * (1 + eccentricity)

class cheta.derived.orbit.DP_ARGUMENT_PERIGEE[source]¶: Orbital element: argument of perigee (degrees)

class cheta.derived.orbit.DP_ASCENDING_NODE[source]¶: Orbital element: right ascension of ascending node (degrees)

class cheta.derived.orbit.DP_ECCENTRICITY[source]¶: Orbital element: eccentricity

class cheta.derived.orbit.DP_INCLINATION[source]¶: Orbital element: inclination (degrees)

class cheta.derived.orbit.DP_MEAN_ANOMALY[source]¶: Orbital element: mean anomaly (degrees)

class cheta.derived.orbit.DP_ORBIT_PERIOD[source]¶: Orbital element: period (sec)

class cheta.derived.orbit.DP_PERIGEE_RADIUS[source]¶

Orbit perigee based on orbital elements (m)

Defined as semi_major_axis * (1 - eccentricity)

class cheta.derived.orbit.DP_SEMI_MAJOR_AXIS[source]¶: Orbital element: semi-major axis (m)

class cheta.derived.orbit.DerivedParameterOrbit[source]¶

calc(data)[source]¶

content_root = 'orbit'¶

get_orbital_element(data)[source]¶

max_gap = 1000.0¶

rootparams = ['orbitephem0_x', 'orbitephem0_y', 'orbitephem0_z', 'orbitephem0_vx', 'orbitephem0_vy', 'orbitephem0_vz']¶

time_step = 328.0¶

cheta.derived.orbit.calc_orbital_elements(x, y, z, vx, vy, vz)[source]¶

Calculate orbital elements given input position (x, y, z) in m and velocity (vx, vy, vz) in m/s. Orbit perigee and apogee are computed as well.

Returns a dict with the following key values:

Key name

Unit

semi_major_axis

m

eccentricity

–

inclination

deg

ascending_node

deg

argument_perigee

deg

mean_anomaly

deg

orbit_period

sec

apogee_radius

m

perigee_radius

m

PCAD¶

Derived parameter MSIDs related to PCAD subsystem.

Author: A. Arvai

Revision History:

   Jan 2012       Initial version
 1 Mar 2012       Modified all ephemeris-based parameters to use predictive
                  ephemeris
26 Mar 2012       Re-defined DP_ROLL_FSS and DP_PITCH_FSS to improve accuracy

class cheta.derived.pcad.DP_CSS1_NPM_SUN[source]¶

Coarse Sun Sensor Counts 1 filtered for NPM and SA Illuminated

Defined as CSS-1 current converted back into counts (AOCSSI1 * 4095 / 5.49549) when in NPM (AOPCADMD==1) and SA is illuminated (AOSAILLM==1). Otherwise, “Bads” flag is set equal to one.

class cheta.derived.pcad.DP_CSS2_NPM_SUN[source]¶

Coarse Sun Sensor Counts 2 filtered for NPM and SA Illuminated

Defined as CSS-2 current converted back into counts (AOCSSI2 * 4095 / 5.49549) when in NPM (AOPCADMD==1) and SA is illuminated (AOSAILLM==1). Otherwise, “Bads” flag is set equal to one.

class cheta.derived.pcad.DP_CSS3_NPM_SUN[source]¶

Coarse Sun Sensor Counts 3 filtered for NPM and SA Illuminated

Defined as CSS-3 current converted back into counts (AOCSSI3 * 4095 / 5.49549) when in NPM (AOPCADMD==1) and SA is illuminated (AOSAILLM==1). Otherwise, “Bads” flag is set equal to one.

class cheta.derived.pcad.DP_CSS4_NPM_SUN[source]¶

Coarse Sun Sensor Counts 4 filtered for NPM and SA Illuminated

Defined as CSS-4 current converted back into counts (AOCSSI4 * 4095 / 5.49549) when in NPM (AOPCADMD==1) and SA is illuminated (AOSAILLM==1). Otherwise, “Bads” flag is set equal to one.

class cheta.derived.pcad.DP_FSS_CSS_ANGLE_DIFF[source]¶

Angle between FSS and CSS Sun Vectors [Deg]

Defined as the angle between the FSS and CSS sun vectors

Calculated by rotating the CSS sun vector from the SA-1 frame to ACA frame then computing the angular difference using the dot product and ARCCOS. “Bads” flag is set equal to one when not in the FSS FOV.

dtype¶: alias of float32

class cheta.derived.pcad.DP_MAN_ANG[source]¶

Maneuver Angle (Total) [deg]

Defined as the angle between the estimated quaternion and the target quaternion during a maneuver.

Computed using the fourth component of the delta quaternion between AOTARQT<N> and AOATTQT<N> when a maneuver is in progress (AOMANEND = NEND), otherwise equal to zero.

dtype¶: alias of float32

class cheta.derived.pcad.DP_ONE_SHOT[source]¶

One Shot [arcsec]

Defined as the RSS of AOATTER2 and AOATTER3 while in NPM and zero for all other PCAD modes.

dtype¶: alias of float32

class cheta.derived.pcad.DP_PITCH[source]¶

Sun Pitch Angle from Predictive Ephemeris in ACA Frame [deg]

Defined as the angle between the sun vector and ACA X-axis.

Calculated using arccos of the sun vector x component in the body frame where the sun vector is from predictive ephemeris [SOLAREPHEM0 and ORBITEPHEM0] and the estimated attitude from the OBC’s estimated quaternion [AOATTQT<n>].

dtype¶: alias of float32

class cheta.derived.pcad.DP_PITCH_CSS[source]¶

Sun Pitch Angle from CSS Data in ACA Frame [Deg]

Defined as the angle between the sun vector and ACA X-axis.

Calculated by rotating the CSS sun vector from the SA-1 frame to ACA frame based on the solar array angles AOSARES1 and AOSARES2.

dtype¶: alias of float32

class cheta.derived.pcad.DP_PITCH_CSS_SA[source]¶

Sun Pitch Angle from CSS Data in SA Frame [Deg]

Defined as the rotation about the SA-1 Y-axis required to align the sun vector with the SA-1 Y-Z plane.

Calculated as 90.0 - ARCCOS(AOSUNSA1).

dtype¶: alias of float32

class cheta.derived.pcad.DP_PITCH_FSS[source]¶

Sun Pitch Angle from FSS Data in ACA Frame [Deg]

Defined as the angle between the sun vector and ACA X-axis.

When in FSS FOV per AOSUNPRS: Calculated using the FSS alpha and beta angles to compute the sun vector in the FSS frame. The sun vector is then rotated into the ACA frame using the rotation matrix (an OBC k-constant). Pitch is computed using the arccos function.

When NOT in FSS FOV per AOSUNPRS: <data>.bads = 1

dtype¶: alias of float32

class cheta.derived.pcad.DP_ROLL[source]¶

Off-Nominal Roll Angle in ACA Frame [Deg]

Defined as the rotation about the ACA X-axis required to align the sun vector with the ACA X/Z plane.

Calculated using the four-quadrant arctan of the sun vector y and z components in the ACA frame where the sun vector is from predictive ephemeris [SOLAREPHEM0 and ORBITEPHEM0] and the estimated attitude from the OBC’s estimated quaternion [AOATTQT<n>].

http://occweb.cfa.harvard.edu/twiki/pub/Aspect/WebHome/ROLLDEV3.pdf

dtype¶: alias of float32

class cheta.derived.pcad.DP_ROLL_CSS[source]¶

Off-Nominal Roll Angle from CSS Data in ACA Frame [Deg]

Defined as the rotation about the ACA X-axis required to align the sun vector with the ACA X/Z plane.

Calculated by rotating the CSS sun vector from the SA-1 frame to ACA frame based on the solar array angles AOSARES1 and AOSARES2.

dtype¶: alias of float32

class cheta.derived.pcad.DP_ROLL_CSS_SA[source]¶

Sun Roll Angle from CSS Data in SA Frame [Deg]

Defined as the rotation about the SA-1 X-axis required to align the sun vector with the SA-1 X-Z plane.

Calculated as ARCTAN( (-1*AOSUNSA2) / (-1*AOSUNSA3) ) using the four-quadrant version of ARCTAN.

dtype¶: alias of float32

class cheta.derived.pcad.DP_ROLL_FSS[source]¶

Off-Nominal Roll Angle from FSS Data in ACA Frame [Deg]

Defined as the rotation about the ACA X-axis required to align the sun vector with the ACA X/Z plane.

When in FSS FOV per AOSUNPRS: Calculated using the FSS alpha and beta angles to compute the sun vector in the FSS frame. The sun vector is then rotated into the ACA frame using the rotation matrix (an OBC k-constant). Roll is computed using the arctan function.

When NOT in FSS FOV per AOSUNPRS: <data>.bads = 1

dtype¶: alias of float32

class cheta.derived.pcad.DP_RW1_DELTA_TEMP[source]¶

Difference between Reaction Wheel 1 Compartment and Bearing Temperature [Deg F]

Defined as TCYZ_RW1 - ARWA1BT.

class cheta.derived.pcad.DP_RW2_DELTA_TEMP[source]¶

Difference between Reaction Wheel 2 Compartment and Bearing Temperature [Deg F]

Defined as TPCP_RW2 - ARWA2BT.

class cheta.derived.pcad.DP_RW3_DELTA_TEMP[source]¶

Difference between Reaction Wheel 3 Compartment and Bearing Temperature [Deg F]

Defined as TPCP_RW3 - ARWA3BT.

class cheta.derived.pcad.DP_RW4_DELTA_TEMP[source]¶

Difference between Reaction Wheel 4 Compartment and Bearing Temperature [Deg F]

Defined as TPCM_RW4 - ARWA4BT.

class cheta.derived.pcad.DP_RW5_DELTA_TEMP[source]¶

Difference between Reaction Wheel 5 Compartment and Bearing Temperature [Deg F]

Defined as TPCM_RW5 - ARWA5BT.

class cheta.derived.pcad.DP_RW6_DELTA_TEMP[source]¶

Difference between Reaction Wheel 6 Compartment and Bearing Temperature [Deg F]

Defined as TCYZ_RW6 - ARWA6BT.

class cheta.derived.pcad.DP_RW_MOM_TOT[source]¶

Total Reaction Wheel Momentum [Ft-Lb-Sec]

Defined as the RSS of AORWMOM1, AORWMOM2, and AORWMOM3.

dtype¶: alias of float32

class cheta.derived.pcad.DP_SA_ANG_AVG[source]¶

Average Solar Array Angle [Deg]

Defined as the mean of AOSARES1 and AOSARES2.

class cheta.derived.pcad.DP_SUN_XZ_ANGLE[source]¶

Angle between Sun and ACA X/Z plane [Deg]

Incidence angle of the Sun vector on the ACA X/Z plane.

Calculated using the four-quadrant arctan of the sun vector y and z components in the ACA frame where the sun vector is from definitive ephemeris [SOLAREPHEM0 and ORBITEPHEM0] and the estimated attitude from the OBC’s estimated quaternion [AOATTQT<n>].

http://occweb.cfa.harvard.edu/twiki/pub/Aspect/WebHome/ROLLDEV3.pdf

dtype¶: alias of float32

class cheta.derived.pcad.DP_SYS_MOM_TOT[source]¶

Total System Momentum [Ft-Lb-Sec]

Defined as the sum of the reaction wheel, environmental, and spacecraft momentum.

Calculated as the RSS of AOSYMOM1, AOSYMOM2, and AOSYMOM3.

cheta.derived.pcad.calc_sun_vec_body_css(data, safe_mode=False)[source]¶

Calculate the normalized sun vector in body coordinates using CSS data.

This relies on the on-board computation of the sun vector in the solar array frame along with the solar array resolver angle.

Parameters:

data – MSIDset with appropriate MSIDs
safe_mode – use safe mode telemetry MSIDs (6* vs AO*)

Returns (sun_vec_norm, bads):

3 x N array of vectors, bads mask

cheta.derived.pcad.qmult(q1, q2)[source]¶

Multiply two quaternions or arrays of quaternions

The input quaternions must have shape of (4,) or (4, N, ..).

Parameters:

q1 – first quaternion
q2 – second quaternion

Returns:

q1*q2 as an array with same shape as q1 and q2

cheta.derived.pcad.qrotate(q, r)[source]¶

Rotate a vector by a quaternion

The input quaternion must have a shape of (4,) or (4, N, ..).

The input vector must have a shape of (3,) or (3, N, ..).

Parameters:

q – quaternion defining the rotation
r – vector to be rotated

:returns r rotated by q as an array with the same shape as r

cheta.derived.pcad.sun_vector_body(data, predictive=True)[source]¶

Calculate the normalized sun vector in body coordinates.

Parameters:

data – MSIDset with orbitephem, solarephem and aoattqt<N> MSIDs
predictive – use predictive ephemeris

Returns:

3 x N array of vectors

Thermal¶

Derived parameter MSIDs related to thermal subsystems.

class cheta.derived.thermal.DP_ABH_DUTYCYCLE[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ53', '4OHTRZ54', '4OHTRZ55', '4OHTRZ57', '4S1PWR05', '4S1PWR06']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_EE_AXIAL[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR58', 'OHRTHR12', 'OHRTHR36', 'OHRTHR56', 'OHRTHR57', 'OHRTHR55', 'OHRTHR35', 'OHRTHR37', 'OHRTHR34', 'OHRTHR13', 'OHRTHR10', 'OHRTHR11']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_EE_BULK[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR10', 'OHRTHR58', 'OHRTHR52', 'OHRTHR53', 'OHRTHR56', 'OHRTHR57', 'OHRTHR54', 'OHRTHR55', 'OHRTHR12', 'OHRTHR35', 'OHRTHR11', 'OHRTHR08', 'OHRTHR09', 'OHRTHR31', 'OHRTHR33', 'OHRTHR34', 'OHRTHR13', 'OHRTHR36', 'OHRTHR37']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_EE_DIAM[source]¶

Kodak diametrical encircled energy

calc(data)[source]¶

rootparams = ['OHRMGRD6', 'OHRMGRD3']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_EE_RADIAL[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR52', 'OHRTHR53', 'OHRTHR54', 'OHRTHR31', 'OHRTHR09', 'OHRTHR08', 'OHRTHR33']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_EE_THERM[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR37', 'OHRTHR58', 'OHRMGRD6', 'OHRMGRD3', 'OHRTHR35', 'OHRTHR52', 'OHRTHR53', 'OHRTHR56', 'OHRTHR57', 'OHRTHR54', 'OHRTHR55', 'OHRTHR12', 'OHRTHR36', 'OHRTHR08', 'OHRTHR09', 'OHRTHR31', 'OHRTHR33', 'OHRTHR34', 'OHRTHR13', 'OHRTHR10', 'OHRTHR11']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_HAAG[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR58', 'OHRTHR12', 'OHRTHR56', 'OHRTHR57', 'OHRTHR55', 'OHRTHR13', 'OHRTHR36', 'OHRTHR37', 'OHRTHR34', 'OHRTHR35', 'OHRTHR10', 'OHRTHR11']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_HADG[source]¶

calc(data)[source]¶

rootparams = ['OHRMGRD3', 'OHRMGRD6']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_HARG[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR52', 'OHRTHR53', 'OHRTHR54', 'OHRTHR31', 'OHRTHR09', 'OHRTHR08', 'OHRTHR33']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_HMAX35[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR52', 'OHRTHR53', 'OHRTHR50', 'OHRTHR51', 'OHRTHR56', 'OHRTHR55', 'OHRTHR23', 'OHRTHR22', 'OHRTHR30', 'OHRTHR33', 'OHRTHR12', 'OHRTHR13', 'OHRTHR10', 'OHRTHR11', 'OHRTHR36', 'OHRTHR37', 'OHRTHR49', 'OHRTHR45', 'OHRTHR44', 'OHRTHR47', 'OHRTHR46', 'OHRTHR42', 'OHRTHR29', 'OHRTHR02', 'OHRTHR05', 'OHRTHR04', 'OHRTHR07', 'OHRTHR06', 'OHRTHR09', 'OHRTHR08', 'OHRTHR21', 'OHRTHR27', 'OHRTHR26', 'OHRTHR25', 'OHRTHR24', 'OHRTHR03']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_HMCSAVE[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR10', 'OHRTHR58', 'OHRTHR52', 'OHRTHR53', 'OHRTHR56', 'OHRTHR57', 'OHRTHR54', 'OHRTHR55', 'OHRTHR12', 'OHRTHR35', 'OHRTHR11', 'OHRTHR08', 'OHRTHR09', 'OHRTHR31', 'OHRTHR33', 'OHRTHR34', 'OHRTHR13', 'OHRTHR36', 'OHRTHR37']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_HMIN35[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR52', 'OHRTHR53', 'OHRTHR50', 'OHRTHR51', 'OHRTHR56', 'OHRTHR55', 'OHRTHR23', 'OHRTHR08', 'OHRTHR30', 'OHRTHR33', 'OHRTHR12', 'OHRTHR13', 'OHRTHR36', 'OHRTHR11', 'OHRTHR10', 'OHRTHR37', 'OHRTHR49', 'OHRTHR45', 'OHRTHR44', 'OHRTHR47', 'OHRTHR46', 'OHRTHR42', 'OHRTHR29', 'OHRTHR02', 'OHRTHR05', 'OHRTHR04', 'OHRTHR07', 'OHRTHR06', 'OHRTHR09', 'OHRTHR22', 'OHRTHR21', 'OHRTHR27', 'OHRTHR26', 'OHRTHR25', 'OHRTHR24', 'OHRTHR03']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_HRMA_AVE[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR52', 'OHRTHR53', 'OHRTHR50', 'OHRTHR51', 'OHRTHR56', 'OHRTHR55', 'OHRTHR09', 'OHRTHR08', 'OHRTHR30', 'OHRTHR33', 'OHRTHR12', 'OHRTHR13', 'OHRTHR10', 'OHRTHR11', 'OHRTHR36', 'OHRTHR37', 'OHRTHR49', 'OHRTHR45', 'OHRTHR44', 'OHRTHR47', 'OHRTHR46', 'OHRTHR42', 'OHRTHR29', 'OHRTHR02', 'OHRTHR05', 'OHRTHR04', 'OHRTHR07', 'OHRTHR06', 'OHRTHR23', 'OHRTHR22', 'OHRTHR21', 'OHRTHR27', 'OHRTHR26', 'OHRTHR25', 'OHRTHR24', 'OHRTHR03']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_HRMHCHK[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR52', 'OHRTHR53', 'OHRTHR50', 'OHRTHR51', 'OHRTHR56', 'OHRTHR55', 'OHRTHR09', 'OHRTHR08', 'OHRTHR30', 'OHRTHR33', 'OHRTHR12', 'OHRTHR13', 'OHRTHR10', 'OHRTHR11', 'OHRTHR36', 'OHRTHR37', 'OHRTHR49', 'OHRTHR45', 'OHRTHR44', 'OHRTHR47', 'OHRTHR46', 'OHRTHR42', 'OHRTHR03', 'OHRTHR02', 'OHRTHR05', 'OHRTHR04', 'OHRTHR07', 'OHRTHR06', 'OHRTHR23', 'OHRTHR22', 'OHRTHR21', 'OHRTHR27', 'OHRTHR26', 'OHRTHR25', 'OHRTHR24', 'OHRTHR29']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OBAAG[source]¶

calc(data)[source]¶

rootparams = ['4RT704T', '4RT705T', '4RT708T', '4RT707T', '4RT709T', '4RT711T', '4RT700T', '4RT702T', '4RT701T', '4RT703T', 'OOBTHR34', 'OOBTHR33', 'OOBTHR31', 'OOBTHR62', 'OOBTHR63', '4RT706T', '4RT710T']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OBAAGW[source]¶

calc(data)[source]¶

rootparams = ['4RT705T', '4RT707T', '4RT709T', '4RT711T', '4RT701T', '4RT703T', 'OOBTHR34', 'OOBTHR33', 'OOBTHR31']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OBACAVE[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR19', 'OOBTHR18', 'OOBTHR15', 'OOBTHR14', 'OOBTHR17', 'OOBTHR11', 'OOBTHR10', 'OOBTHR13', 'OOBTHR12', 'OOBTHR30', 'OOBTHR08', 'OOBTHR09', 'OOBTHR24', 'OOBTHR25', 'OOBTHR26', 'OOBTHR27', 'OOBTHR20', 'OOBTHR21', 'OOBTHR22', 'OOBTHR23', 'OOBTHR28', 'OOBTHR29']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OBACAVEW[source]¶

calc(data)[source]¶

rootparams = ['4RT705T', 'OOBTHR19', '4RT707T', 'OOBTHR15', 'OOBTHR14', '4RT711T', 'OOBTHR11', 'OOBTHR10', 'OOBTHR13', '4RT701T', 'OOBTHR34', 'OOBTHR33', 'OOBTHR31', 'OOBTHR30', 'OOBTHR18', '4RT709T', '4RT703T', 'OOBTHR17', 'OOBTHR08', 'OOBTHR09', 'OOBTHR24', 'OOBTHR25', 'OOBTHR26', 'OOBTHR27', 'OOBTHR20', 'OOBTHR21', 'OOBTHR22', 'OOBTHR23', 'OOBTHR12', 'OOBTHR28', 'OOBTHR29']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OBADIG[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR08', 'OOBTHR19', 'OOBTHR31', 'OOBTHR13', 'OOBTHR26', 'OOBTHR34', 'OOBTHR33', 'OOBTHR22', 'OOBTHR23', 'OOBTHR60', 'OOBTHR61', 'OOBTHR28', 'OOBTHR29']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OBADIGW[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR08', '4RT705T', 'OOBTHR19', '4RT707T', 'OOBTHR22', '4RT711T', 'OOBTHR13', '4RT701T', 'OOBTHR26', 'OOBTHR34', 'OOBTHR33', 'OOBTHR31', 'OOBTHR23', 'OOBTHR60', 'OOBTHR61', 'OOBTHR28', 'OOBTHR29']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OBA_AVE[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR19', 'OOBTHR18', 'OOBTHR15', 'OOBTHR14', 'OOBTHR17', 'OOBTHR11', 'OOBTHR10', 'OOBTHR13', 'OOBTHR12', 'OOBTHR37', 'OOBTHR36', 'OOBTHR35', 'OOBTHR34', 'OOBTHR33', 'OOBTHR31', 'OOBTHR30', 'OOBTHR39', 'OOBTHR38', 'OOBTHR08', 'OOBTHR09', 'OOBTHR24', 'OOBTHR25', 'OOBTHR26', 'OOBTHR27', 'OOBTHR20', 'OOBTHR21', 'OOBTHR22', 'OOBTHR23', 'OOBTHR46', 'OOBTHR44', 'OOBTHR45', 'OOBTHR28', 'OOBTHR29', 'OOBTHR40', 'OOBTHR41']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OMAX34[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR19', 'OOBTHR18', 'OOBTHR15', 'OOBTHR14', 'OOBTHR17', 'OOBTHR11', 'OOBTHR10', 'OOBTHR13', 'OOBTHR12', 'OOBTHR37', 'OOBTHR36', 'OOBTHR35', 'OOBTHR34', 'OOBTHR33', 'OOBTHR31', 'OOBTHR30', 'OOBTHR39', 'OOBTHR38', 'OOBTHR28', 'OOBTHR08', 'OOBTHR09', 'OOBTHR24', 'OOBTHR25', 'OOBTHR26', 'OOBTHR27', 'OOBTHR20', 'OOBTHR21', 'OOBTHR22', 'OOBTHR23', 'OOBTHR46', 'OOBTHR45', 'OOBTHR42', 'OOBTHR29', 'OOBTHR40', 'OOBTHR41']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_OMIN34[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR19', 'OOBTHR18', 'OOBTHR15', 'OOBTHR14', 'OOBTHR17', 'OOBTHR11', 'OOBTHR10', 'OOBTHR13', 'OOBTHR12', 'OOBTHR37', 'OOBTHR36', 'OOBTHR35', 'OOBTHR34', 'OOBTHR33', 'OOBTHR31', 'OOBTHR30', 'OOBTHR39', 'OOBTHR38', 'OOBTHR28', 'OOBTHR08', 'OOBTHR09', 'OOBTHR24', 'OOBTHR25', 'OOBTHR26', 'OOBTHR27', 'OOBTHR20', 'OOBTHR21', 'OOBTHR22', 'OOBTHR23', 'OOBTHR46', 'OOBTHR45', 'OOBTHR42', 'OOBTHR29', 'OOBTHR40', 'OOBTHR41']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_P01[source]¶

Zone 1 heater power

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ01']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P02[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ02']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P03[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ03']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P04[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ04']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P05[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ05', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P06[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ06', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P07[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ07', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P08[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ08']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P09[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ09']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P10[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ10']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P11[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ11']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P12[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ12']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P13[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ13']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P14[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ14', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P15[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ15', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P16[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ16', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P17[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ17']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P18[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ18']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P19[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ19']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P20[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ20']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P23[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ23']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P24[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ24', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P25[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ25', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P26[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ26', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P27[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ27', 'ELBV', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P28[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ28', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P29[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ29', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P30[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ30', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P31[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ31']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P32[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ32', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P33[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ33', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P34[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ34']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P35[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ35', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P36[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ36']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P37[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ37']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P38[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ38', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P39[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ39']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P40[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ40']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P41[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ41', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P42[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ42', 'ELBV', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P43[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ43', 'ELBV', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P44[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ44', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P45[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ45', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P46[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ46', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P47[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ47', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P48[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ48', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P49[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ49', 'ELBV', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P50[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ50', 'ELBV', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P51[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ51', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P52[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ52', 'ELBV', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P53[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ53', 'ELBV', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P54[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ54', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P55[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ55']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P57[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ57']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P58[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ58', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P59[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ59', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P60[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ60', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P61[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ61', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P62[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ62', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P63[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ63', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P64[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ64']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P65[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ65']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P66[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ66']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P67[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ67']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P68[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ68', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P69[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ69', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P75[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ75']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P76[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ76', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P77[source]¶

calc(data)[source]¶

rootparams = ['4OHTRZ77', 'ELBV']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P78[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ78']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P79[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ79']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_P80[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ80']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PABH[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ53', '4OHTRZ54', '4OHTRZ55', '4OHTRZ57', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PAFTCONE[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ48', '4OHTRZ49', '4OHTRZ50', '4OHTRZ51', '4OHTRZ52', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PAFTCYL[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ66', '4OHTRZ67', '4OHTRZ68']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PAHP[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ11', '4OHTRZ12', '4OHTRZ13']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PCONE[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ61', '4OHTRZ62', '4OHTRZ63']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PFAP[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ01', '4OHTRZ02', '4OHTRZ03', '4OHTRZ04', '4OHTRZ05', '4OHTRZ06', '4OHTRZ07']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PFWDCONE[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ31', '4OHTRZ32', '4OHTRZ33', '4OHTRZ34', '4OHTRZ35', '4OHTRZ36', '4OHTRZ37', '4OHTRZ38', '4OHTRZ39', '4OHTRZ40', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PFWDCYL[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ58', '4OHTRZ59', '4OHTRZ60']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PHRMA[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ01', '4OHTRZ02', '4OHTRZ03', '4OHTRZ04', '4OHTRZ05', '4OHTRZ06', '4OHTRZ07', '4OHTRZ08', '4OHTRZ09', '4OHTRZ10', '4OHTRZ11', '4OHTRZ12', '4OHTRZ13', '4OHTRZ14', '4OHTRZ15', '4OHTRZ16', '4OHTRZ17', '4OHTRZ18', '4OHTRZ19', '4OHTRZ20', '4OHTRZ23', '4OHTRZ24']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PHRMASTRUTS[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ25', '4OHTRZ26', '4OHTRZ27', '4OHTRZ28', '4OHTRZ29', '4OHTRZ30', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PIC[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ23', '4OHTRZ24']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PMIDCONE[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ41', '4OHTRZ42', '4OHTRZ43', '4OHTRZ44', '4OHTRZ45', '4OHTRZ46', '4OHTRZ47', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PMNT[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ14', '4OHTRZ15', '4OHTRZ16']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_POBAT[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ25', '4OHTRZ26', '4OHTRZ27', '4OHTRZ28', '4OHTRZ29', '4OHTRZ30', '4OHTRZ31', '4OHTRZ32', '4OHTRZ33', '4OHTRZ34', '4OHTRZ35', '4OHTRZ36', '4OHTRZ37', '4OHTRZ38', '4OHTRZ39', '4OHTRZ40', '4OHTRZ41', '4OHTRZ42', '4OHTRZ43', '4OHTRZ44', '4OHTRZ45', '4OHTRZ46', '4OHTRZ47', '4OHTRZ48', '4OHTRZ49', '4OHTRZ50', '4OHTRZ51', '4OHTRZ52', '4OHTRZ53', '4OHTRZ54', '4OHTRZ55', '4OHTRZ57', '4OHTRZ75', '4OHTRZ76', '4OHTRZ77', '4OHTRZ78', '4OHTRZ79', '4OHTRZ80', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_POC[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ17', '4OHTRZ18', '4OHTRZ19']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PPL10[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ08', '4OHTRZ09', '4OHTRZ10']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PRADVNT[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ64', '4OHTRZ65', '4OHTRZ69']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PSCSTRUTS[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ75', '4OHTRZ76', '4OHTRZ77', '4OHTRZ78', '4OHTRZ79', '4OHTRZ80']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PTFTE[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ58', '4OHTRZ59', '4OHTRZ60', '4OHTRZ61', '4OHTRZ62', '4OHTRZ63', '4OHTRZ64', '4OHTRZ65', '4OHTRZ66', '4OHTRZ67', '4OHTRZ68', '4OHTRZ69']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_PTOTAL[source]¶

calc(data)[source]¶

rootparams = ['ELBV', '4OHTRZ01', '4OHTRZ02', '4OHTRZ03', '4OHTRZ04', '4OHTRZ05', '4OHTRZ06', '4OHTRZ07', '4OHTRZ08', '4OHTRZ09', '4OHTRZ10', '4OHTRZ11', '4OHTRZ12', '4OHTRZ13', '4OHTRZ14', '4OHTRZ15', '4OHTRZ16', '4OHTRZ17', '4OHTRZ18', '4OHTRZ19', '4OHTRZ20', '4OHTRZ23', '4OHTRZ24', '4OHTRZ25', '4OHTRZ26', '4OHTRZ27', '4OHTRZ28', '4OHTRZ29', '4OHTRZ30', '4OHTRZ31', '4OHTRZ32', '4OHTRZ33', '4OHTRZ34', '4OHTRZ35', '4OHTRZ36', '4OHTRZ37', '4OHTRZ38', '4OHTRZ39', '4OHTRZ40', '4OHTRZ41', '4OHTRZ42', '4OHTRZ43', '4OHTRZ44', '4OHTRZ45', '4OHTRZ46', '4OHTRZ47', '4OHTRZ48', '4OHTRZ49', '4OHTRZ50', '4OHTRZ51', '4OHTRZ52', '4OHTRZ53', '4OHTRZ54', '4OHTRZ55', '4OHTRZ57', '4OHTRZ58', '4OHTRZ59', '4OHTRZ60', '4OHTRZ61', '4OHTRZ62', '4OHTRZ63', '4OHTRZ64', '4OHTRZ65', '4OHTRZ66', '4OHTRZ67', '4OHTRZ68', '4OHTRZ69', '4OHTRZ75', '4OHTRZ76', '4OHTRZ77', '4OHTRZ78', '4OHTRZ79', '4OHTRZ80', '4S1PWR05', '4S1PWR06']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_SUNANGLE[source]¶

calc(data)[source]¶

rootparams = ['AOSARES1']¶

time_step = 0.25625¶

class cheta.derived.thermal.DP_TABMAX[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR47', 'OOBTHR42', 'OOBTHR43']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TABMIN[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR47', 'OOBTHR42', 'OOBTHR43']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TELAB_AVE[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR47', 'OOBTHR42', 'OOBTHR43']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TELHS_AVE[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR02', 'OOBTHR03', 'OOBTHR06', 'OOBTHR07', 'OOBTHR04', 'OOBTHR05']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TELSS_AVE[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR51', 'OOBTHR50', 'OOBTHR53', 'OOBTHR52', 'OOBTHR54', 'OOBTHR49']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_THSMAX[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR02', 'OOBTHR03', 'OOBTHR06', 'OOBTHR07', 'OOBTHR04', 'OOBTHR05']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_THSMIN[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR02', 'OOBTHR03', 'OOBTHR06', 'OOBTHR07', 'OOBTHR04', 'OOBTHR05']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TILT_AXIAL[source]¶

calc(data)[source]¶

rootparams = ['OOBAGRD3']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TILT_BULK[source]¶

calc(data)[source]¶

rootparams = ['OHRTHR43', 'OHRTHR42']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TILT_DIAM[source]¶

calc(data)[source]¶

rootparams = ['OOBAGRD6']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TILT_MAX[source]¶

calc(data)[source]¶

rootparams = ['OOBAGRD6', 'OOBAGRD3', 'OHRTHR43', 'OHRTHR42']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TILT_RSS[source]¶

calc(data)[source]¶

rootparams = ['OOBAGRD6', 'OOBAGRD3', 'OHRTHR43', 'OHRTHR42']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TSSMAX[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR51', 'OOBTHR50', 'OOBTHR53', 'OOBTHR52', 'OOBTHR54', 'OOBTHR49']¶

time_step = 32.8¶

class cheta.derived.thermal.DP_TSSMIN[source]¶

calc(data)[source]¶

rootparams = ['OOBTHR51', 'OOBTHR50', 'OOBTHR53', 'OOBTHR52', 'OOBTHR54', 'OOBTHR49']¶

time_step = 32.8¶

class cheta.derived.thermal.DerivedParameterThermal[source]¶

content_root = 'thermal'¶

fix_4OHTRZ50(data)[source]¶

fix_bus_5_and_bus_6(data)[source]¶

Computed MSIDs¶

Cheta provides support for on-the-fly computed MSIDs with the following features:

Designed for simplicity and ease of use by non-expert Ska3 users.
Following a simple recipe in user code, new MSIDs are automatically registered to fetch.
Computed MSIDs can included embedded parameters to allow further customization or application of a function to any other MSID.
Support for 5-minute and daily stats also included.

See the Built-in computed MSIDs and API section for a list of the available computed MSIDs.

See the DAWG computed MSIDs notebook for a longer introduction to the topic with a number of examples.

What’s the advantage?¶

If you have a function of MSID values, what is the advantage of going through this formalism to create a computed MSID instead of just using the function output directly?

It gives you the entire fetch API! You get for free all the fetch bling like plotting, selecting intervals, kadi event integration, interpolation.
If your computed MSID is useful to the community it is simple to add to the released cheta package for other users.
It allows creation of arbitrary MSIDs that can be used in xija models. Examples: * pm2tv1t_clean as a Node component * cmd_state_acisfp_temp_32 as a telemetry input (TelemData component).

Example¶

This example is not terribly useful but illustrates the key concepts and requirements for defining a computed MSID:

from cheta.derived.comps import ComputedMsid  # Inherit from this class

# Class name is arbitrary, but by convention start with `Comp_`
class Comp_Val_Plus_Offset(ComputedMsid):
    """
    Computed MSID to add an integer offset to MSID value.

    MSID format is "<MSID>_plus_<offset>", where <MSID> is an existing MSID
    in the archive and <offset> is an integer offset.

    """
    msid_match = r'(\w+)_plus_(\d+)'

    # `msid_match` is a class attribute that defines a regular expresion to
    # match for this computed MSID.  This must be defined and it must be
    # unambiguous (not matching an existing MSID or other computed MSID).
    #
    # The two groups in parentheses specify the arguments <MSID> and <offset>.
    # These are passed to `get_msid_attrs` as msid_args[0] and msid_args[1].
    # The \w symbol means to match a-z, A-Z, 0-9 and underscore (_).
    # The \d symbol means to match digits 0-9.

    def get_msid_attrs(self, tstart, tstop, msid, msid_args):
        """
        Get attributes for computed MSID: ``vals``, ``bads``, ``times``,
        ``unit``, ``raw_vals``, and ``offset``.  The first four must always
        be provided.

        :param tstart: start time (CXC secs)
        :param tstop: stop time (CXC secs)
        :param msid: full MSID name e.g. tephin_plus_5
        :param msid_args: tuple of regex match groups (msid_name,)
        :returns: dict of MSID attributes
        """
        # Process the arguments parsed from the MSID
        msid  msid_args[0]
        offset = int(msid_args[1])

        # Get the raw telemetry value in user-requested unit system
        dat = self.fetch_sys.MSID(msid, tstart, tstop)

        # Do the computation
        vals = dat.vals + offset

        # Return a dict with at least `vals`, `times`, `bads`, and `unit`.
        # Additional attributes are allowed and will be set on the
        # final MSID object.
        out = {'vals': vals,
                'bads': dat.bads,
                'times': dat.times,
                'unit': dat.unit,
                'vals_raw': dat.vals,  # Provide original values without offset
                'offset': offset  # Provide the offset for reference
                }
        return out

Units¶

Computed MSIDs should support units where applicable. In the example above this was done by using self.fetch_sys in order to get the original data in the user-requested system. In other words, if the user did a call dat = fetch_sci.Msid('msid_plus_8', '2010:001', '2010:002'), then self.fetch_sys would translate to self.fetch_sci.

In some cases the unit handling may require additional specification. This can happen if the computation needs to be done in a particular unit, as is the case for the built-in Comp_MUPS_Valve_Temp_Clean class. Here the class must define an additional units attribute with the following structure:

units = {
    # Unit system for attrs from get_msid_attrs(), one of 'eng', 'sci', 'cxc'
    'internal_system': 'eng',

    # Units for eng, sci, cxc systems
    'eng': 'DEGF',
    'sci': 'DEGC',
    'cxc': 'K',

    # Attributes that need conversion.  At least `vals` but maybe others.
    'convert_attrs': ['vals']
}

The specified units must all be convertable using functions defined in the converters dict in the cheta.units module.

Built-in computed MSIDs and API¶

Support computed MSIDs in the cheta archive.

ComputedMsid: base class for user-generated comps.

Comp_MUPS_Valve_Temp_Clean: Cleaned MUPS valve temperatures MSIDs (pm2thv1t|pm1thv2t)_clean.

Comp_KadiCommandState: Commanded states cmd_state_<key>_<dt> for any kadi commanded state value.

Comp_Quat: Quaternions

quat_aoattqt = AOATTQT[1-4]

quat_aoatupq = AOATUPQ[1-3]

quat_aocmdqt = AOCMDQT[1-3]

quat_aotarqt = AOTARQT[1-3]

Comp_Pitch_Roll_OBC_Safe: Sun Pitch pitch_comp and off-nominal roll roll_comp which are valid in NPNT, NMAN, NSUN and safe mode.

See: https://nbviewer.jupyter.org/urls/cxc.harvard.edu/mta/ASPECT/ipynb/misc/DAWG-mups-valve-xija-filtering.ipynb
class cheta.derived.comps.Comp_KadiCommandState(unit_system='eng')[source]¶
Computed MSID for kadi dynamic commanded states.

The MSID here takes the form cmd_state_<state_key>_<dt> where:

state_key is a valid commanded state key such as pitch or pcad_mode or acisfp_temp.

dt is the sampling time expressed as a multiple of 1.025 sec frames.

Example MSID names:
'cmd_state_pcad_mode_1': sample ``pcad_mode`` every 1.025 secs
'cmd_state_acisfp_temp_32': sample ``acisfp_temp`` every 32.8 secs
get_msid_attrs(tstart, tstop, msid, msid_args)[source]¶

Get attributes for computed MSID: vals, bads, times

Parameters:

tstart – start time (CXC secs)

tstop – stop time (CXC secs)

msid – full MSID name e.g. cmd_state_pitch_clean

msid_args – tuple of regex match groups: (state_key, dt)

Returns:

dict of MSID attributes
class cheta.derived.comps.Comp_MUPS_Valve_Temp_Clean(unit_system='eng')[source]¶

Computed MSID for cleaned MUPS valve temps PM2THV1T, PM1THV2T

This uses the cleaning method demonstrated in the following notebook to return a version of the MUPS valve temperature comprised of telemetry values that are consistent with a thermal model.

https://nbviewer.jupyter.org/urls/cxc.cfa.harvard.edu/mta/ASPECT/ipynb/misc/mups-valve-xija-filtering.ipynb https://nbviewer.jupyter.org/urls/cxc.harvard.edu/mta/ASPECT/ipynb/misc/DAWG-mups-valve-xija-filtering.ipynb

Allowed MSIDs are ‘pm2thv1t_clean’ and ‘pm1thv2t_clean’ (as always case is not important). Optionally one can include the chandra_models branch name, tag or commit hash to used for reading the MUPS 1B and MUPS 2A thermal model specifications. For example you can use ‘pm1thv2t_clean_3.28’ to get the model from release 3.28 of chandra_models.

get_msid_attrs(tstart, tstop, msid, msid_args)[source]¶

Get attributes for computed MSID: vals, bads, times, unit

Parameters:

tstart – start time (CXC secs)

tstop – stop time (CXC secs)

msid – full MSID name e.g. pm2thv1t_clean

msid_args – tuple of regex match groups (msid_name,)

Returns:

dict of MSID attributes
class cheta.derived.comps.Comp_Pitch_Roll_OBC_Safe(unit_system='eng')[source]¶
Computed MSID to return pitch or off-nominal roll angle which is valid in NPNT, NMAN, NSUN, and Safe Mode.

MSID names are pitch_comp and roll_comp.

The computation logic is shown below:
On OBC control (CONLOFP == "NRML"):
  - AOPCADMD in ["NPNT", "NMAN"] => compute pitch/roll from AOATTQT[1234]
    (MAUDE or CXC) and predictive ephemeris ORBITEPHEM0_[XYZ] and
    SOLAREPHEM0_[XYZ] (CXC only but always available)
  - AOPCADMD == "NSUN" => get pitch/roll from PITCH/ROLL_CSS derived params.
    These are also in MAUDE.

On CPE control (CONLOFP == "SAFE"):
  - Compute pitch/roll from 6SUNSA[123] + 6SARES[12] via calc_pitch/roll_css_safe()

Intervals for other CONLOFP values are ignored.
get_msid_attrs(tstart, tstop, msid, msid_args)[source]¶

Get attributes for computed MSID: vals, bads, times, unit, raw_vals, and offset. The first four must always be provided.

Parameters:

tstart – start time (CXC secs)

tstop – stop time (CXC secs)

msid – full MSID name e.g. tephin_plus_5

msid_args – tuple of regex match groups (msid_name,)

Returns:

dict of MSID attributes
class cheta.derived.comps.Comp_Quat(unit_system='eng')[source]¶
Computed MSID for returning the quaternion telemetry as a Quat object

This defines the following computed MSIDs based on the corresponding TDB MSIDs:

quat_aoattqt = AOATTQT[1-4]

quat_aoatupq = AOATUPQ[1-3]

quat_aocmdqt = AOCMDQT[1-3]

quat_aotarqt = AOTARQT[1-3]

Example:
>>> from cheta import fetch
>>> qatt = fetch.Msid('quat_aoattqt', '2022:001:00:00:00', '2022:001:00:00:04')
>>> qatt.vals
Quat(array([[-0.07434856, -0.55918674, -0.80432653,  0.18665828],
            [-0.07434854, -0.55918679, -0.8043265 ,  0.18665825],
            [-0.07434849, -0.55918674, -0.80432653,  0.18665829],
            [-0.07434849, -0.55918667, -0.80432653,  0.18665852]]))
>>> qatt.vals.equatorial
array([[193.28905806,  19.16894296,  67.36207683],
       [193.28905485,  19.1689407 ,  67.36208471],
       [193.28906329,  19.16893787,  67.36207699],
       [193.28908839,  19.16895134,  67.36206404]])
This computed MSID can be used with the MAUDE data source. Be aware that if the telemetry has a missing VCDU then there is a risk of getting a slightly incorrect quaternion. This would occur since the code uses nearest-neighbor interpolation to associate the four components of the quaternion with a single time. For back-orbit data this is rare, but for real-time data it is more likely.

get_msid_attrs(tstart: float, tstop: float, msid: str, msid_args: tuple)[source]¶

Get the attributes required for this MSID.

Get attributes for computed MSID, which must include at least vals, bads, times, and may include additional attributes.

This MUST be supplied by sub-classes.

Parameters:

tstart – start time (CXC secs)

tstop – stop time (CXC secs)

msid – full MSID name e.g. tephin_plus_5

msid_args – tuple of regex match groups (msid_name,)

Returns:

dict of MSID attributes
class cheta.derived.comps.ComputedMsid(unit_system='eng')[source]¶

Base class for cheta computed MSID.

Sub-classes must define at least the following:

msid_match class attribute as a regex to match for the MSID.

get_msid_attrs() method to perform the computation and return a dict with the result.

Optionally:

units attribute to specify unit handling.

See the fetch tutorial Computed MSIDs section for details.

convert_units(msid_attrs)[source]¶

Convert required elements of msid_attrs to self.unit_system.

Unit_system can be one of ‘cxc’, ‘sci’, ‘eng’.

Parameters:

msid_attrs – dict, input MSID attributes

unit_system – str, unit system

Returns:

dict, converted MSID attributes

property fetch_cxc¶

Fetch in CXC (FITS standard) units like K

property fetch_eng¶

Fetch in TDB engineering units like DEGF

property fetch_sci¶

Fetch in scientific units like DEGC

property fetch_sys¶

Fetch in the unit system specified for the class

classmethod get_matching_comp_cls(msid)[source]¶

Get computed classes that match msid

Parameters:

msid – str, input msid

Returns:

first ComputedMsid subclass that matches msid or None

get_msid_attrs(tstart, tstop, msid, msid_args)[source]¶

Get the attributes required for this MSID.

Get attributes for computed MSID, which must include at least vals, bads, times, and may include additional attributes.

This MUST be supplied by sub-classes.

Parameters:

tstart – start time (CXC secs)

tstop – stop time (CXC secs)

msid – full MSID name e.g. tephin_plus_5

msid_args – tuple of regex match groups (msid_name,)

Returns:

dict of MSID attributes

get_stats_attrs(tstart, tstop, msid, match_args, interval)[source]¶

Get 5-min or daily stats attributes.

This is normally not overridden by sub-classes.

Parameters:

tstart – start time (CXC secs)

tstop – stop time (CXC secs)

msid – full MSID name e.g. tephin_plus_5

msid_args – tuple of regex match groups (msid_name,)

Returns:

dict of MSID attributes

Pseudo-MSIDs in the engineering archive¶

ACIS DEA housekeeping¶

Ephemeris¶

HRC Secondary Science and Housekeeping¶

Invalid data¶

Querying data¶

HK MSIDs¶

SS MSIDs¶

Science Instrument Module¶

SEA standard telemetry¶

SEA diagnostic telemetry¶

SIMCOOR (CXC high-level values)¶

EPHIN¶

Derived Parameters or Calcs¶

Available MSIDs¶

Definition¶

ACIS Power¶

EPS¶

Orbital elements¶

PCAD¶

Thermal¶

Computed MSIDs¶

What’s the advantage?¶

Example¶

Units¶

Built-in computed MSIDs and API¶

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Key name	Unit
semi_major_axis	m
eccentricity	–
inclination	deg
ascending_node	deg
argument_perigee	deg
mean_anomaly	deg
orbit_period	sec
apogee_radius	m
perigee_radius	m

Navigation

Pseudo-MSIDs in the engineering archive¶

ACIS DEA housekeeping¶

Ephemeris¶

HRC Secondary Science and Housekeeping¶

Invalid data¶

Querying data¶

HK MSIDs¶

SS MSIDs¶

Science Instrument Module¶

SEA standard telemetry¶

SEA diagnostic telemetry¶

SIMCOOR (CXC high-level values)¶

EPHIN¶

Derived Parameters or Calcs¶

Available MSIDs¶

Definition¶

ACIS Power¶

EPS¶

Orbital elements¶

PCAD¶

Thermal¶

Computed MSIDs¶

What’s the advantage?¶

Example¶

Units¶

Built-in computed MSIDs and API¶