vania-janet/multiturn-rag-retrieval-data huggingface dataset:
","duration":1616,"name":"delay","t0":2240},{"ch":"m12","label":"M_m12","name":"parametric_pulse","parameters":{"amp":0.2689282414516953,0.07795896965518907],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m12","duration":1616,"name":"delay","t0":2240},{"ch":"m13","label":"M_m13","name":"parametric_pulse","parameters":{"amp":-0.18586146902424347,-0.16645048612770755],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m13","duration":1616,"name":"delay","t0":2240},{"ch":"m14","label":"M_m14","name":"parametric_pulse","parameters":{"amp":0.24405471924768304,0.304199431315929],"duration":2240,"sigma":64,"width":1984
":{"ch":"d25","name":"fc","phase":"-(P2)","t0":0},{"ch":"d25","label":"X90p_d25","name":"parametric_pulse","parameters":{"amp":0.09948311069139505,0.001740498997220154],"beta":-1.5580294307932967,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d25","name":"fc","phase":"-(P0)","t0":160},{"ch":"d25","label":"X90m_d25","name":"parametric_pulse","parameters":{"amp":-0.09948311069139505,-0.0017404989972201476],"beta":-1.5580294307932967,"duration":160,"sigma":40},"pulse_shape":"drag","t0":160},{"ch":"d25","name":"fc","phase":"-(P1)","t0":320},{"ch":"u47","name":"fc","phase":"-(P2)","t0":0},{"ch":"u47","name":"fc","phase":"-(P0)","
"beta":-1.9472277991959055,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0}]},{"name":"x","qubits":26],"sequence":{"ch":"d26","label":"Xp_d26","name":"parametric_pulse","parameters":{"amp":0.2214873491024295,0],"beta":-1.1817358508886309,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0}]}],"discriminator":{"name":"hw_qmfk","params":{}},"meas_freq
":0.19808719711323416,0],"beta":-1.9472277991959055,"duration":160,"sigma":40},"pulse_shape":"drag","t0":1488},{"ch":"d25","label":"Y90m_d25","name":"parametric_pulse","parameters":{"amp":0.0017404989972201414,-0.09948311069139505],"beta":-1.5580294307932967,"duration":160,"sigma":40},"pulse_shape":"drag","t0":2976},{"ch":"u42","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u42","name":"fc","phase":-1.5707963267948966,"t0":2976},{"ch":"u47","name":"fc","phase":-1.5707963267948966,"t0":0},{"ch":"u51","name":"fc","phase":-1.5707963267948966,"t0":0},{"ch":"u52","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u52","label":"CR90p_u52",
phase":-1.5707963267948966,"t0":0},{"ch":"u51","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u51","label":"CR90p_u51","name":"parametric_pulse","parameters":{"amp":0.15063508627208172,-0.3796959362508614],"duration":608,"sigma":64,"width":352},"pulse_shape":"gaussian_square","t0":160},{"ch":"u51","label":"CR90m_u51","name":"parametric_pulse","parameters":{"amp":-0.15063508627208166,0.3796959362508614],"duration":608,"sigma":64,"width":352},"pulse_shape":"gaussian_square","t0":928},{"ch":"u51","name":"fc","phase":-1.5707963267948966,"t0":1536},{"ch":"u53","name":"fc","phase":-1.5707963267948966,"t0":0},{"ch":"u55","name":"fc","phase":-3.141592653589793,"t0":0},{"
Let us consider a simple time series created from an in-memory list:

values = [1.0, 2.0, 4.0]
x = tspy.time_series(values)
x

This returns:

TimeStamp: 0 Value: 1.0
TimeStamp: 1 Value: 2.0
TimeStamp: 2 Value: 4.0

At construction time, the time series can be associated with a TRS. Associating a TRS with a time series allows its numeric timestamps to be as per the time tick and offset/timezone. The following example shows 1 minute and 1 Jan 2019, 12 midnight (GMT):

zdt = datetime.datetime(2019,1,1,0,0,0,0,tzinfo=datetime.timezone.utc)
x_trs = tspy.time_series(data, time_tick=datetime.timedelta(minutes=1), start_time=zdt)
x_trs

This returns:

TimeStamp: 2019-01-01T00:00Z Value: 1.0
TimeStamp: 2019-01-01T00:01Z Value: 2.0
TimeStamp: 2019-01-01T00:02Z Value: 4.0

Here is another example where the numeric timestamps are reinterpreted with a time tick of one hour and offset/timezone as 1 Jan 2019, 12 midnight US Eastern Daylight Savings time (EDT).

tz_edt = datetime.timezone.edt
zdt = datetime.datetime(2019,1,1,0,0,0,0,tzinfo=tz_edt)
x_trs = tspy.time_series(data, time_tick=datetime.timedelta(hours=1), start_time=zdt)
x_trs

This returns:

TimeStamp: 2019-01-01T00:00-04:00 Value: 1.0
TimeStamp: 2019-01-01T00:01-04:00 Value: 2.0
","name":"fc","phase":-3.141592653589793,"t0":1600},{"ch":"u8","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u8","label":"CX_u8","name":"parametric_pulse","parameters":{"amp":0.20617080384731395,0.12777263833077593],"duration":1440,"sigma":64,"width":1184},"pulse_shape":"gaussian_square","t0":160},{"ch":"u8","name":"fc","phase":-0.1410171958813371,"t0":1600},{"ch":"u8","name":"fc","phase":-3.141592653589793,"t0":1600}]},{"name":"cx","qubits":2,1],"sequence":{"ch":"d1","name":"CX_d1_u4","t0":0},{"ch":"d1","name":"fc","phase":-0.10752540111995057,"t0":864},{"ch":"d2","name":"fc","phase":3.0579512328042373,"t0":
"parameters":{"amp":0.10435209762961972,0.0004178817129556554],"beta":-1.0957411302075677,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d19","label":"Xp_d19","name":"parametric_pulse","parameters":{"amp":0.2092139461623777,0],"beta":-0.8915174347061647,"duration":160,"sigma":40},"pulse_shape":"drag","t0":1024},{"ch":"d19","label":"Y90m_d19","name":"parametric_pulse","parameters":{"amp":0.0004178817129556769,-0.10435209762961972],"beta":-1.0957411302075677,"duration":160,"sigma":40},"pulse_shape":"drag","t0":2048},{"ch":"d20","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"d20","label":"Y90p_d20","name":"parametric_pulse","parameters":{"amp":-0.0012405319959103154,0.08906994860879265],"beta":-0.5292445408258966,"duration":160,"sigma":
,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d21","label":"Xp_d21","name":"parametric_pulse","parameters":{"amp":0.20178632799569557,0],"beta":-1.3661947892326112,"duration":160,"sigma":40},"pulse_shape":"drag","t0":1040},{"ch":"d21","label":"Y90m_d21","name":"parametric_pulse","parameters":{"amp":0.005197567638426465,-0.10054543378303288],"beta":1.9474086217974163,"duration":160,"sigma":40},"pulse_shape":"drag","t0":2080},{"ch":"d23","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"d23","label":"Y90p_d23","name":"parametric_pulse","parameters":{"amp":-0.0012581539911796155,0.10024010876056792],"beta":-0.660782248902255,"duration":160,"sigma":40},"pulse_shape":"drag","t0":
8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":3],"sequence":{"ch":"m3","label":"M_m3","name":"parametric_pulse","parameters":{"amp":-0.24827290464195859,0.07720469429150624],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m3","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":4]
","phase":-3.141592653589793,"t0":1136},{"ch":"u32","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u32","name":"fc","phase":-0.08096603909239776,"t0":1136},{"ch":"u32","name":"fc","phase":-3.141592653589793,"t0":1136}]},{"name":"cx","qubits":12,15],"sequence":{"ch":"d12","name":"fc","phase":1.5707963267948966,"t0":0},{"ch":"d12","label":"Ym_d12","name":"parametric_pulse","parameters":{"amp":-2.821894792282436e-17,-0.15361679760766664],"beta":-0.8316746390488242,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d12","label":"Xp_d12","name":"parametric_pulse","parameters":{"amp":0.15361679760766664,0],"beta":-0.8316746390488242,"duration":160,"sigma":40},"
,"label":"M_m1","name":"parametric_pulse","parameters":{"amp":0.1570406590513299,-0.19452051666784137],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m1","duration":1616,"name":"delay","t0":2240},{"ch":"m10","label":"M_m10","name":"parametric_pulse","parameters":{"amp":-0.005050814263327484,-0.3399624821583661],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m10","duration":1616,"name":"delay","t0":2240},{"ch":"m11","label":"M_m11","name":"parametric_pulse","parameters":{"amp":0.05817055972291986,-0.355269173981535],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m11
":{"amp":0.20001343493861812,0],"beta":-1.2349202191509674,"duration":160,"sigma":40},"pulse_shape":"drag","t0":768},{"ch":"d24","label":"Y90m_d24","name":"parametric_pulse","parameters":{"amp":0.00120425505210453,-0.09981430549526288],"beta":-0.9853100504653961,"duration":160,"sigma":40},"pulse_shape":"drag","t0":1536},{"ch":"d25","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"d25","label":"Y90p_d25","name":"parametric_pulse","parameters":{"amp":-0.001740498997220154,0.09948311069139505],"beta":-1.5580294307932967,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d25","label":"CR90p_d25_u51","name":"parametric_pulse","parameters":{"amp":0.05679908133545926,0.0032660204201901033],"duration":608,"sigma":64,"width
"fc","phase":1.5707963267948966,"t0":0},{"ch":"d21","label":"Ym_d21","name":"parametric_pulse","parameters":{"amp":-3.706754710374398e-17,-0.20178632799569557],"beta":-1.3661947892326112,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d21","label":"Xp_d21","name":"parametric_pulse","parameters":{"amp":0.20178632799569557,0],"beta":-1.3661947892326112,"duration":160,"sigma":40},"pulse_shape":"drag","t0":832},{"ch":"u39","name":"fc","phase":1.5707963267948966,"t0":0},{"ch":"u44","label":"CR90p_u44","name":"parametric_pulse","parameters":{"amp":-0.040518765638158684,0.13976663508725476],"duration":672,"sigma":64,"width":416},"pulse_shape":"gaussian_square","t0":160},{"ch":"u44","
,"name":"fc","phase":-1.5707963267948966,"t0":1664},{"ch":"d18","label":"X90p_d18","name":"parametric_pulse","parameters":{"amp":0.10515991526809636,0.002363533204347135],"beta":-2.1494178582769803,"duration":160,"sigma":40},"pulse_shape":"drag","t0":1664},{"ch":"d21","name":"fc","phase":-1.5707963267948966,"t0":0},{"ch":"d21","label":"X90p_d21","name":"parametric_pulse","parameters":{"amp":0.10054543378303288,0.00519756763842647],"beta":1.9474086217974163,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d21","label":"Xp_d21","name":"parametric_pulse","parameters":{"amp":0.20178632799569557,0],"beta":-1.3661947892326112,"duration":160,"sigma":40},"pulse_shape":"drag","t0":832},{"ch":"d21",
parameters":{"amp":0.059954379699145625,0.34482672801697584],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m17","duration":1616,"name":"delay","t0":2240},{"ch":"m18","label":"M_m18","name":"parametric_pulse","parameters":{"amp":0.04695872889448773,-0.35692419052316177],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m18","duration":1616,"name":"delay","t0":2240},{"ch":"m19","label":"M_m19","name":"parametric_pulse","parameters":{"amp":-0.33603973513852214,-0.09786366234747149],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m19","duration":1616,"name":"delay","t0":2240},{"ch
:2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":10],"sequence":{"ch":"m10","label":"M_m10","name":"parametric_pulse","parameters":{"amp":-0.005050814263327484,-0.3399624821583661],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m10","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t
":{"amp":-0.0008795219808534919,0.07673912449813387],"beta":-0.8894045718580408,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d12","name":"CX_d12_u27","t0":160},{"ch":"d12","name":"fc","phase":-0.08096603909239776,"t0":1136},{"ch":"d12","name":"fc","phase":-3.141592653589793,"t0":1136},{"ch":"d12","label":"Y90p_d12","name":"parametric_pulse","parameters":{"amp":-0.0008795219808534919,0.07673912449813387],"beta":-0.8894045718580408,"duration":160,"sigma":40},"pulse_shape":"drag","t0":1136},{"ch":"d13","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"d13","label":"Y90p_d13","name":"parametric_pulse","parameters":{"amp":-0.00010598104638801333,0.10100084229826041],"beta":-0.350966939975829,
:-0.8915174347061647,"duration":160,"sigma":40},"pulse_shape":"drag","t0":4960},{"ch":"d19","label":"Y90m_d19","name":"parametric_pulse","parameters":{"amp":0.0004178817129556769,-0.10435209762961972],"beta":-1.0957411302075677,"duration":160,"sigma":40},"pulse_shape":"drag","t0":9920},{"ch":"u31","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u31","name":"fc","phase":-1.5707963267948966,"t0":9920},{"ch":"u35","name":"fc","phase":-1.5707963267948966,"t0":0},{"ch":"u40","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u40","label":"CR90p_u40","name":"parametric_pulse","parameters":{"amp":0.02815089505502904,-0.017242784545556257],"duration":4800,"sigma":64,"width":4544}
","name":"parametric_pulse","parameters":{"amp":0.056779930738404864,0.0010262859248659304],"duration":624,"sigma":64,"width":368},"pulse_shape":"gaussian_square","t0":160},{"ch":"d17","label":"CR90m_d17_u38","name":"parametric_pulse","parameters":{"amp":-0.056779930738404864,-0.0010262859248659235],"duration":624,"sigma":64,"width":368},"pulse_shape":"gaussian_square","t0":944},{"ch":"d18","name":"fc","phase":1.5707963267948966,"t0":0},{"ch":"d18","label":"Ym_d18","name":"parametric_pulse","parameters":{"amp":-3.867080956982288e-17,-0.2105140823555385],"beta":-1.8964675940415894,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d18","label":"Xp_d18","name":"parametric_pulse","parameters":{"amp":0.2105140823555385,0
","label":"CR90m_u11","name":"parametric_pulse","parameters":{"amp":-0.29194119808298125,0.22330667147027602],"duration":496,"sigma":64,"width":240},"pulse_shape":"gaussian_square","t0":816},{"ch":"u11","name":"fc","phase":-1.5707963267948966,"t0":1312},{"ch":"u16","name":"fc","phase":-1.5707963267948966,"t0":0},{"ch":"u19","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u19","name":"fc","phase":-1.5707963267948966,"t0":1312},{"ch":"u22","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u22","name":"fc","phase":-1.5707963267948966,"t0":1312},{"ch":"u7","name":"fc","phase":-1.5707963267948966,"t0":0}]},{"name":"cx","qubits"
","label":"X90m_d18","name":"parametric_pulse","parameters":{"amp":-0.10515991526809636,-0.00236353320434712],"beta":-2.1494178582769803,"duration":160,"sigma":40},"pulse_shape":"drag","t0":160},{"ch":"d18","name":"fc","phase":"-(P1)","t0":320},{"ch":"u33","name":"fc","phase":"-(P2)","t0":0},{"ch":"u33","name":"fc","phase":"-(P0)","t0":160},{"ch":"u33","name":"fc","phase":"-(P1)","t0":320},{"ch":"u36","name":"fc","phase":"-(P2)","t0":0},{"ch":"u36","name":"fc","phase":"-(P0)","t0":160},{"ch":"u36","name":"fc","phase":"-(P1)","t0":320},{"ch":"u44","name":"fc","phase":"-(P2)","t0
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"ts2": tspy.time_series([5.0, 2.0, 4.0, 5.0])
})

The output is the following:

ts2 time series
------------------------------
TimeStamp: 0 Value: 5.0
TimeStamp: 1 Value: 2.0
TimeStamp: 2 Value: 4.0
TimeStamp: 3 Value: 5.0
ts1 time series
------------------------------
TimeStamp: 0 Value: 1.0
TimeStamp: 1 Value: 2.0
TimeStamp: 2 Value: 3.0





 Interpreting time 

By default, a time series uses a long data type to denote when a given observation was created, which is referred to as a time tick. A time reference system is used for time series with timestamps that are human interpretable. See [Using time reference system](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-time-reference-system).

The following example shows how to create a simple time series where each index denotes a day after the start time of 1990-01-01:

import datetime
granularity = datetime.timedelta(days=1)
start_time = datetime.datetime(1990, 1, 1, 0, 0, 0, 0, tzinfo=datetime.timezone.utc)

ts = tspy.time_series([5.0, 2.0, 4.0, 6.0, 6.0, 7.0], granularity=granularity, start_time=start_time)
ts

The output is as follows:

TimeStamp: 1990-01-01T00:00Z Value: 5.0
TimeStamp: 1990-01-02T00:00Z Value: 2.0
d25","name":"fc","phase":1.5707963267948966,"t0":0},{"ch":"d25","label":"Ym_d25","name":"parametric_pulse","parameters":{"amp":-3.638802778451895e-17,-0.19808719711323416],"beta":-1.9472277991959055,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d25","label":"Xp_d25","name":"parametric_pulse","parameters":{"amp":0.19808719711323416,0],"beta":-1.9472277991959055,"duration":160,"sigma":40},"pulse_shape":"drag","t0":1488},{"ch":"u47","name":"fc","phase":1.5707963267948966,"t0":0},{"ch":"u51","name":"fc","phase":1.5707963267948966,"t0":0},{"ch":"u52","label":"CR90p_u52","name":"parametric_pulse","parameters":{"amp":0.020899963779518582,-0.16964690041333844],"duration":1328,"sigma":
ts = tspy.time_series([5.0, 2.0, 4.0, 6.0, 6.0, 7.0], granularity=granularity, start_time=start_time)
ts

The output is as follows:

TimeStamp: 1990-01-01T00:00Z Value: 5.0
TimeStamp: 1990-01-02T00:00Z Value: 2.0
TimeStamp: 1990-01-03T00:00Z Value: 4.0
TimeStamp: 1990-01-04T00:00Z Value: 6.0
TimeStamp: 1990-01-05T00:00Z Value: 6.0
TimeStamp: 1990-01-06T00:00Z Value: 7.0





 Performing simple transformations 

Transformations are functions which, when given one or more time series, return a new time series.

For example, to segment a time series into windows where each window is of size=3, sliding by 2 records, you can use the following method:

window_ts = ts.segment(3, 2)
window_ts

The output is as follows:

TimeStamp: 0 Value: original bounds: (0,2) actual bounds: (0,2) observations: [(0,5.0),(1,2.0),(2,4.0)]
TimeStamp: 2 Value: original bounds: (2,4) actual bounds: (2,4) observations: [(2,4.0),(3,6.0),(4,6.0)]

This example shows adding 1 to each value in a time series:

add_one_ts = ts.map(lambda x: x + 1)
add_one_ts

The output is as follows:

TimeStamp: 0 Value: 6.0
TimeStamp: 1 Value: 3.0
TimeStamp: 2 Value: 5.0
TimeStamp: 3 Value: 7.0
TimeStamp: 4 Value: 7.0
TimeStamp: 5 Value: 8.0
"u3","qubits":11],"sequence":{"ch":"d11","name":"fc","phase":"-(P2)","t0":0},{"ch":"d11","label":"X90p_d11","name":"parametric_pulse","parameters":{"amp":0.10356307461691308,0.0015104680315885467],"beta":-0.8534632024962311,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d11","name":"fc","phase":"-(P0)","t0":160},{"ch":"d11","label":"X90m_d11","name":"parametric_pulse","parameters":{"amp":-0.10356307461691308,-0.0015104680315885248],"beta":-0.8534632024962311,"duration":160,"sigma":40},"pulse_shape":"drag","t0":160},{"ch":"d11","name":"fc","phase":"-(P1)","t0":320},{"ch":"u18","name":"fc","phase":"-(P2)","t0":0},{"ch":"u18","
},{"ch":"m23","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":24],"sequence":{"ch":"m24","label":"M_m24","name":"parametric_pulse","parameters":{"amp":0.1440670610966128,0.35163145750485014],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m24","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1
Use this command to configure the specified IBM Cloud Monitoring service instance to receive platform metrics. When run, the command prompts the user to keep an existing service instance or replace it with the specified instance.

ibmcloud monitoring platform-metrics-receiver --name NAME [--region REGION] [--force]



 Command options 

--name <NAME> | --n <NAME>
: Name of the instance.

--region <REGION> | -r <REGION>
: Name of the region, for example, us-south or eu-gb. If not specified, the region logged into or targeted is used.

--force
: Supresses the prompt and replaces the existing platform metric instance with the instance specified by --name.

--help | -h
: List options available for the command.





 Examples 

The following are examples using the ibmcloud monitoring platform-metrics-receiver command.

Configures the IBM Cloud Monitoring abc as the instance to receive platform metrics.

ibmcloud monitoring platform-metrics-receiver --name "IBM Cloud Monitoring abc"

Configures the IBM Cloud Monitoring abc in the eu-gb region as the instance to receive platform metrics.

ibmcloud monitoring platform-metrics-receiver --name "IBM Cloud Monitoring abc" --region eu-gb







 ibmcloud monitoring dashboard list 

Use this command to list all the dashboards that are visible to a user in a Monitoring instance.

ibmcloud monitoring dashboard list --name NAME [--region REGION] [--output FORMAT] [--team TEAM_NAME]



 Command options 

--name <NAME> | --sn <NAME>
: Name of the instance.

--region <REGION> | -r <REGION>
: Name of the region, for example, us-south or eu-gb. If not specified, the region logged into or targeted will be used.

--output <TYPE>
: A comma-separated list of output preferences enclosed in double-quotes ("). If only a single preference is specified, the double-quotes can be omitted. Supported options are WIDE and JSON.
},"pulse_shape":"gaussian_square","t0":0},{"ch":"m0","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"sequence":{"ch":"m0","label":"M_m0","name":"parametric_pulse","parameters":{"amp":0.1471357509043359,0.26144037715289353],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m0","duration":1616,"name":"delay","t0":2240},{"ch":"m1"
,"sequence":{"ch":"m4","label":"M_m4","name":"parametric_pulse","parameters":{"amp":-0.13039505703052454,-0.25903113539110745],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0":0},{"ch":"m4","duration":1616,"name":"delay","t0":2240},{"duration":2240,"memory_slot":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"name":"acquire","qubits":0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"t0":0}]},{"name":"measure","qubits":5],"sequence":{"ch":"m5","label":"M_m5","name":"parametric_pulse","parameters":{"amp":-0.2602175328900784,-0.24877064854319902],"duration":2240,"sigma":64,"width":1984},"pulse_shape":"gaussian_square","t0"