Continuous waveform mode¶
In this tutorial we will demonstrate continuous waveform mode (see Continuous waveform mode). In addition, we will observe the output on an oscilloscope to demonstrate the results.
This tutorial is designed with the Pulsar QCM in mind, but can easily be used with a Pulsar QRM as well. Change any Pulsar QCM reference to Pulsar QRM and only use one sequencer.
Setup¶
First, we are going to import the required packages and connect to the instrument.
[1]:
#Set up the environment.
import pprint
import os
import scipy.signal
import math
import json
import matplotlib.pyplot
import numpy
from pulsar_qcm.pulsar_qcm import pulsar_qcm
#Connect to the Pulsar QCM at default IP address.
pulsar = pulsar_qcm("qcm", "192.168.0.2")
#Reset the instrument for good measure.
pulsar.reset()
print("Status:")
print(pulsar.get_system_status())
Status:
{'status': 'OKAY', 'flags': []}
Generate waveforms¶
Next, we are going to generate a couple of waveforms that we are going to upload to the instrument in the next step.
[2]:
#Waveform parameters
waveform_length = 120 #nanoseconds (needs to be a multiple of 4 ns)
#Waveform dictionary (data will hold the samples and index will be used to select the waveforms in the instrument).
waveforms = {
"gaussian": {"data": [], "index": 0},
"sine": {"data": [], "index": 1},
"sawtooth": {"data": [], "index": 2},
"dc": {"data": [], "index": 3}
}
#Create gaussian waveform
if "gaussian" in waveforms:
waveforms["gaussian"]["data"] = scipy.signal.gaussian(waveform_length, std=0.12 * waveform_length)
#Create sine waveform
if "sine" in waveforms:
waveforms["sine"]["data"] = [math.sin((2 * math.pi / (0.5 * waveform_length)) * i ) for i in range(0, waveform_length//2)]
#Create sawtooth waveform
if "sawtooth" in waveforms:
waveforms["sawtooth"]["data"] = [(1.0 / (waveform_length)) * i for i in range(0, waveform_length)]
#Create DC waveform
if "dc" in waveforms:
waveforms["dc"]["data"] = [1.0 for i in range(0, 4)] #DC only needs to 4 samples and can be repeated
Let’s plot the waveforms to see what we have created.
[3]:
time = numpy.arange(0, max(map(lambda d: len(d["data"]), waveforms.values())), 1)
fig, ax = matplotlib.pyplot.subplots(1,1, figsize=(10, 10/1.61))
ax.set_ylabel("Waveform primitive amplitude")
ax.set_xlabel("Time (ns)")
for wf, d in waveforms.items():
ax.plot(time[:len(d["data"])], d["data"], ".-", linewidth=0.5, label=wf)
ax.legend(loc=4)
ax.yaxis.grid()
ax.xaxis.grid()
matplotlib.pyplot.draw()
matplotlib.pyplot.show() # add this at EOF to prevent execution stall
Upload waveforms¶
Now that we know that the waveforms are what we expect them to be, let’s upload them to the instrument. To do this we need to store the waveforms in a JSON file together with a Q1ASM program for the sequence processor. Since we are going to use continuous waveform mode, the sequence processor will be bypassed and the Q1ASM program can be trivial (i.e. stop).
[4]:
#Sequence program.
seq_prog = "stop"
#Reformat waveforms to lists if necessary.
for name in waveforms:
if str(type(waveforms[name]["data"]).__name__) == "ndarray":
waveforms[name]["data"] = waveforms[name]["data"].tolist() # JSON only supports lists
assert (len(waveforms[name]["data"]) % 4) == 0, "In continuous waveform mode the lenght of a waveform must be a multiple of 4!"
#Add sequence program and waveforms to single dictionary and write to JSON file.
wave_and_prog_dict = {"waveforms": {"awg": waveforms}, "program": seq_prog}
with open("cont_wave_mode.json", 'w', encoding='utf-8') as file:
json.dump(wave_and_prog_dict, file, indent=4)
file.close()
Let’s write the JSON file to the instruments. We will use sequencer 0 and 1, which will drive outputs \(\text{O}^{[1-2]}\) and \(\text{O}^{[3-4]}\) respectively.
[5]:
#Upload waveforms and programs.
pulsar.sequencer0_waveforms_and_program(os.path.join(os.getcwd(), "cont_wave_mode.json"))
pulsar.sequencer1_waveforms_and_program(os.path.join(os.getcwd(), "cont_wave_mode.json"))
Play waveforms¶
The waveforms have been uploaded to the instrument. Now we need to configure the instrument to run in continuous waveform mode. We do this by setting the following parameters of the sequencers.
[6]:
#Configure the sequencers to run in continuous waveform mode.
for sequencer in range(0, 2):
pulsar.set("sequencer{}_cont_mode_en_awg_path0".format(sequencer), True) #On outputs O1 and O3.
pulsar.set("sequencer{}_cont_mode_en_awg_path1".format(sequencer), True) #On outputs O2 and O4.
#Set specific waveform to specific output.
pulsar.sequencer0_cont_mode_waveform_idx_awg_path0(0) #Gaussian on O1
pulsar.sequencer0_cont_mode_waveform_idx_awg_path1(1) #Sine on O2
pulsar.sequencer1_cont_mode_waveform_idx_awg_path0(2) #Sawtooth on 03
pulsar.sequencer1_cont_mode_waveform_idx_awg_path1(3) #DC on O4
Now let’s start playback.
[7]:
#Arm and start both sequencers.
pulsar.arm_sequencer()
pulsar.start_sequencer()
#Print status of both sequencers (should now say Q1 stopped, because of the stop instruction).
print("Status:")
print(pulsar.get_sequencer_state(0))
print(pulsar.get_sequencer_state(1))
Status:
{'status': 'Q1 STOPPED', 'flags': []}
{'status': 'Q1 STOPPED', 'flags': []}
Check waveforms¶
The instrument is now running in continuous waveform mode. Now let’s connect an oscilloscope and check the outputs. We connect all output channels of the Pulsar QCM to four channels of an oscilloscope. On the scope we are able to see that all waveforms are being generated correctly:
Outputs: Yellow=\(\text{O}^{1}\), Blue=\(\text{O}^{2}\), Purple=\(\text{O}^{3}\) and Green=\(\text{O}^{4}\)
Stop¶
Finally, let’s stop the playback and close the instrument connection.
[8]:
#Stop both sequencers.
pulsar.stop_sequencer()
#Print status of both sequencers (should now say it is stopped).
print("Status:")
print(pulsar.get_sequencer_state(0))
print(pulsar.get_sequencer_state(1))
print()
#Print an overview of the instrument parameters.
print("Snapshot:")
pulsar.print_readable_snapshot(update=True)
#Close the instrument connection.
pulsar.close()
Status:
{'status': 'STOPPED', 'flags': ['FORCED STOP']}
{'status': 'STOPPED', 'flags': ['FORCED STOP']}
Snapshot:
qcm:
parameter value
--------------------------------------------------------------------------------
IDN : {'manufacturer': 'Qblox', 'devi...
reference_source : internal
sequencer0_cont_mode_en_awg_path0 : True
sequencer0_cont_mode_en_awg_path1 : True
sequencer0_cont_mode_waveform_idx_awg_path0 : 0
sequencer0_cont_mode_waveform_idx_awg_path1 : 1
sequencer0_gain_awg_path0 : 1
sequencer0_gain_awg_path1 : 1
sequencer0_marker_ovr_en : False
sequencer0_marker_ovr_value : 0
sequencer0_mod_en_awg : False
sequencer0_nco_freq : 0 (Hz)
sequencer0_nco_phase_offs : 0 (Degrees)
sequencer0_offset_awg_path0 : 0
sequencer0_offset_awg_path1 : 0
sequencer0_sync_en : False
sequencer0_upsample_rate_awg_path0 : 0
sequencer0_upsample_rate_awg_path1 : 0
sequencer0_waveforms_and_program : C:\Users\jordy\Projects\pulsar_...
sequencer1_cont_mode_en_awg_path0 : True
sequencer1_cont_mode_en_awg_path1 : True
sequencer1_cont_mode_waveform_idx_awg_path0 : 2
sequencer1_cont_mode_waveform_idx_awg_path1 : 3
sequencer1_gain_awg_path0 : 1
sequencer1_gain_awg_path1 : 1
sequencer1_marker_ovr_en : False
sequencer1_marker_ovr_value : 0
sequencer1_mod_en_awg : False
sequencer1_nco_freq : 0 (Hz)
sequencer1_nco_phase_offs : 0 (Degrees)
sequencer1_offset_awg_path0 : 0
sequencer1_offset_awg_path1 : 0
sequencer1_sync_en : False
sequencer1_upsample_rate_awg_path0 : 0
sequencer1_upsample_rate_awg_path1 : 0
sequencer1_waveforms_and_program : C:\Users\jordy\Projects\pulsar_...