notebook: update examples
refactor into import, simulation and plotting 3 codeblocks remove extra config for wrapper add docs string for RNG change adpll_period & start_up_delay unit
This commit is contained in:
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0b724e84da
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7c4a680787
@ -39,74 +39,76 @@
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"from plotly.subplots import make_subplots\n",
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"import plotly.graph_objects as go\n",
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"import numpy as np\n",
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"from wave_gen import square_arr\n",
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"from wrpll import WRPLL_simulator\n",
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"\n",
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"from wrpll import WRPLL_simulator"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# settings\n",
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"timestep = 4e-10 # even number is recommended to avoid strange glitches\n",
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"total_steps = 300_000_000\n",
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"timestep = 1e-10\n",
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"total_steps = 200_000_000\n",
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"sim_mode = \"both\"\n",
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"adpll_period = int(100e-6/timestep) # in simulation steps, 100μs is minimum, smaller = more frequency adjustment and filter calulation per unit time\n",
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"start_up_delay = int(100e-6/timestep) # in simulation steps, the frequency adjustment is DISABLE until steps > start_up_delay\n",
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"adpll_period = 100e-6 # in seconds, the period that pll will trigger, (minimum > the sampling rate of collector)\n",
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"start_up_delay = 100e-6 # in seconds, the frequency adjustment is DISABLE until time > start_up_delay\n",
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"\n",
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"gtx_freq = 125_001_519\n",
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"\n",
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"helper_init_freq = gtx_freq * (4096-1)/4096\n",
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"\n",
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"helper_filter = {\n",
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" \"KP\": 2,\n",
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" \"KI\": 4,\n",
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" \"KI\": 0.5,\n",
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" \"KD\": 0,\n",
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"}\n",
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"\n",
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"main_filter = { \n",
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"main_filter = {\n",
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" \"KP\": 12,\n",
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" \"KI\": 0,\n",
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" \"KD\": 0,\n",
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"}\n",
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"\n",
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"\n",
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"t = np.linspace(0, timestep*total_steps, total_steps)\n",
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"\n",
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"# simulation will start with\n",
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"# - random phase for main & helper\n",
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"# - gussian based base_adpll error\n",
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"# - gussian jitter for gtx, main and helper with the set standard deviation\n",
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"# simulation have RNG for\n",
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"# - gtx, main and helper jitter\n",
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"# - starting phase for main and helper\n",
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"# - base_adpll error\n",
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"\n",
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"wrpll_sim = WRPLL_simulator(\n",
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" time=t,\n",
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" timestep=timestep,\n",
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" total_steps=total_steps,\n",
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" sim_mode=sim_mode,\n",
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" helper_filter=helper_filter,\n",
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" main_filter=main_filter,\n",
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" gtx_freq=gtx_freq,\n",
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" adpll_write_period=adpll_period,\n",
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" start_up_delay=start_up_delay,\n",
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" helper_init_freq=helper_init_freq,\n",
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" # preset\n",
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" gtx_jitter_SD=19e-12, # 0 = no jitter\n",
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" dcxo_jitter_SD=9e-12,\n",
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" dcxo_freq=125_000_000,\n",
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" freq_acquisition_SD=100,\n",
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" N=4096, # hardware used 4096\n",
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" blind_period=300, # 300 is used to remove most glitches in simulation (for details see README 'Limitation')\n",
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" cycle_slip_comp=True,\n",
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")\n",
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"wrpll_sim.run()\n",
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"\n",
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"wrpll_sim.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# faster than pyplot with resampling feature\n",
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"# see https://github.com/predict-idlab/plotly-resampler\n",
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"\n",
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"fig = FigureWidgetResampler(make_subplots(rows=4, shared_xaxes=True))\n",
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"fig.add_trace(go.Scattergl(name='phase error'), hf_x=t, hf_y=wrpll_sim.phase_err, row=1, col=1)\n",
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"fig.add_trace(go.Scattergl(name='phase error'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.phase_err, row=1, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='freq error (ppm)'), hf_x=t, hf_y=(\n",
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"fig.add_trace(go.Scattergl(name='freq error (ppm)'), hf_x=wrpll_sim.time, hf_y=(\n",
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" wrpll_sim.mainfreq-gtx_freq) * (1e6/gtx_freq), row=2, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='period error'), hf_x=t, hf_y=wrpll_sim.period_err, row=3, col=1)\n",
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"fig.add_trace(go.Scattergl(name='period error'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.period_err, row=3, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='gtx'), hf_x=t, hf_y=wrpll_sim.gtx+1, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='main'), hf_x=t, hf_y=wrpll_sim.main, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='helper'), hf_x=t, hf_y=wrpll_sim.helper-1, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='gtx'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.gtx+1, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='main'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.main, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='helper'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.helper-1, row=4, col=1)\n",
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"\n",
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"\n",
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"fig.update_layout(\n",
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@ -39,21 +39,27 @@
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"from plotly.subplots import make_subplots\n",
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"import plotly.graph_objects as go\n",
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"import numpy as np\n",
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"from wave_gen import square_arr\n",
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"from wrpll import WRPLL_simulator\n",
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"\n",
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"from wrpll import WRPLL_simulator"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# settings\n",
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"timestep = 4e-10 # even number is recommended to avoid strange glitches\n",
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"total_steps = 200_000_000\n",
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"timestep = 1e-10\n",
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"total_steps = 100_000_000\n",
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"sim_mode = \"helper_pll\"\n",
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"adpll_period = int(100e-6/timestep) # in simulation steps, 100μs is minimum, smaller = more frequency adjustment and filter calulation per unit time\n",
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"start_up_delay = int(100e-6/timestep) # in simulation steps, the frequency adjustment is DISABLE until steps > start_up_delay\n",
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"adpll_period = 100e-6 # in seconds, the period that pll will trigger, (minimum > the sampling rate of collector)\n",
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"start_up_delay = 100e-6 # in seconds, the frequency adjustment is DISABLE until time > start_up_delay\n",
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"\n",
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"gtx_freq = 125_001_519\n",
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"\n",
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"helper_filter = {\n",
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" \"KP\": 2,\n",
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" \"KI\": 4,\n",
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" \"KI\": 0.5,\n",
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" \"KD\": 0,\n",
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"}\n",
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"\n",
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@ -63,53 +69,46 @@
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" \"KD\": 0,\n",
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"}\n",
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"\n",
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"\n",
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"t = np.linspace(0, timestep*total_steps, total_steps)\n",
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"\n",
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"# simulation will start with\n",
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"# - random phase for main & helper\n",
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"# - gussian based base_adpll error\n",
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"# - gussian jitter for gtx, main and helper with the set standard deviation\n",
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"# simulation have RNG for\n",
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"# - gtx, main and helper jitter\n",
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"# - starting phase for main and helper\n",
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"# - base_adpll error\n",
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"\n",
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"wrpll_sim = WRPLL_simulator(\n",
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" time=t,\n",
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" timestep=timestep,\n",
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" total_steps=total_steps,\n",
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" sim_mode=sim_mode,\n",
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" helper_filter=helper_filter,\n",
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" main_filter=main_filter,\n",
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" gtx_freq=gtx_freq,\n",
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" adpll_write_period=adpll_period,\n",
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" start_up_delay=start_up_delay,\n",
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" # preset\n",
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" gtx_jitter_SD=19e-12, # 0 = no jitter\n",
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" dcxo_jitter_SD=9e-12,\n",
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" dcxo_freq=125_000_000,\n",
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" freq_acquisition_SD=500,\n",
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" N=4096, # hardware used 4096\n",
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" blind_period=300, # 300 is used to remove most glitches in simulation (for details see README 'Limitation')\n",
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" cycle_slip_comp=True,\n",
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")\n",
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"\n",
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"wrpll_sim.run()\n",
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"\n",
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"wrpll_sim.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# faster than pyplot with resampling feature\n",
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"# see https://github.com/predict-idlab/plotly-resampler\n",
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"\n",
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"fig = FigureWidgetResampler(make_subplots(rows=3, shared_xaxes=True))\n",
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"fig.add_trace(go.Scattergl(name='period error'), hf_x=t, hf_y=wrpll_sim.period_err, row=1, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='gtx'), hf_x=t, hf_y=wrpll_sim.gtx+1, row=2, col=1)\n",
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"fig.add_trace(go.Scattergl(name='main'), hf_x=t, hf_y=wrpll_sim.main, row=2, col=1)\n",
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"fig.add_trace(go.Scattergl(name='helper'), hf_x=t, hf_y=wrpll_sim.helper-1, row=2, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='helper'), hf_x=t, hf_y=wrpll_sim.helper_adpll, row=3, col=1)\n",
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"fig = FigureWidgetResampler(make_subplots(rows=2, shared_xaxes=True))\n",
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"fig.add_trace(go.Scattergl(name='period error'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.period_err, row=1, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='gtx'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.gtx+1, row=2, col=1)\n",
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"fig.add_trace(go.Scattergl(name='main'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.main, row=2, col=1)\n",
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"fig.add_trace(go.Scattergl(name='helper'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.helper-1, row=2, col=1)\n",
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"\n",
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"fig.update_layout(\n",
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" xaxis2=dict(title=\"time (sec)\"),\n",
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"\n",
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" yaxis1=dict(title=\"beating period error\"),\n",
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" yaxis2=dict(title=\"Signal\"),\n",
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" height=500,\n",
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" height=1000,\n",
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" showlegend=True,\n",
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" title_text=\"PLL example\",\n",
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" legend=dict(\n",
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@ -42,15 +42,21 @@
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"from plotly.subplots import make_subplots\n",
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"import plotly.graph_objects as go\n",
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"import numpy as np\n",
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"from wave_gen import square_arr\n",
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"from wrpll import WRPLL_simulator\n",
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"\n",
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"from wrpll import WRPLL_simulator"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# settings\n",
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"timestep = 4e-10 # even number is recommended to avoid strange glitches\n",
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"total_steps = 200_000_000\n",
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"timestep = 1e-10\n",
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"total_steps = 100_000_000\n",
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"sim_mode = \"main_pll\"\n",
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"adpll_period = int(100e-6/timestep) # in simulation steps, 100μs is minimum, smaller = more frequency adjustment and filter calulation per unit time\n",
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"start_up_delay = int(100e-6/timestep) # in simulation steps, the frequency adjustment is DISABLE until steps > start_up_delay\n",
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"adpll_period = 100e-6 # in seconds, the period that pll will trigger, (minimum > the sampling rate of collector)\n",
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"start_up_delay = 100e-6 # in seconds, the frequency adjustment is DISABLE until time > start_up_delay\n",
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"\n",
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"gtx_freq = 125_001_519\n",
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"\n",
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@ -58,58 +64,57 @@
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"\n",
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"helper_filter = { # unused\n",
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" \"KP\": 2,\n",
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" \"KI\": 4,\n",
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" \"KI\": 0.5,\n",
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" \"KD\": 0,\n",
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"}\n",
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"\n",
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"main_filter = { \n",
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"main_filter = {\n",
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" \"KP\": 12,\n",
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" \"KI\": 0,\n",
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" \"KD\": 0,\n",
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"}\n",
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"\n",
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"\n",
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"t = np.linspace(0, timestep*total_steps, total_steps)\n",
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"# simulation have RNG for\n",
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"# - gtx, main and helper jitter\n",
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"# - starting phase for main and helper\n",
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"# - base_adpll error\n",
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"\n",
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"# simulation will start with\n",
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"# - random phase for main & helper\n",
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"# - gussian based base_adpll error\n",
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"# - gussian jitter for gtx, main and helper with the set standard deviation\n",
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"\n",
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"wrpll_sim = WRPLL_simulator(\n",
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" time=t,\n",
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" timestep=timestep,\n",
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" total_steps=total_steps,\n",
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" sim_mode=sim_mode,\n",
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" helper_filter=helper_filter,\n",
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" main_filter=main_filter,\n",
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" gtx_freq=gtx_freq,\n",
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" adpll_write_period=adpll_period,\n",
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" start_up_delay=start_up_delay,\n",
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" helper_init_freq=helper_init_freq,\n",
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" # preset\n",
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" gtx_jitter_SD=19e-12, # 0 = no jitter\n",
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" dcxo_jitter_SD=9e-12,\n",
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" dcxo_freq=125_000_000,\n",
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" freq_acquisition_SD=100,\n",
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" N=4096, # hardware used 4096\n",
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" blind_period=300, # 300 is used to remove most glitches in simulation (for details see README 'Limitation')\n",
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" cycle_slip_comp=True,\n",
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" helper_init_freq=helper_init_freq\n",
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")\n",
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"wrpll_sim.run()\n",
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"\n",
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"wrpll_sim.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# faster than pyplot with resampling feature\n",
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"# see https://github.com/predict-idlab/plotly-resampler\n",
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"\n",
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"fig = FigureWidgetResampler(make_subplots(rows=4, shared_xaxes=True))\n",
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"fig.add_trace(go.Scattergl(name='phase error'), hf_x=t, hf_y=wrpll_sim.phase_err, row=1, col=1)\n",
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"fig.add_trace(go.Scattergl(name='phase error'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.phase_err, row=1, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='freq error (ppm)'), hf_x=t, hf_y=(\n",
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"fig.add_trace(go.Scattergl(name='freq error (ppm)'), hf_x=wrpll_sim.time, hf_y=(\n",
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" wrpll_sim.mainfreq-gtx_freq) * (1e6/gtx_freq), row=2, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='period error'), hf_x=t, hf_y=wrpll_sim.period_err, row=3, col=1)\n",
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"fig.add_trace(go.Scattergl(name='period error'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.period_err, row=3, col=1)\n",
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"\n",
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"fig.add_trace(go.Scattergl(name='gtx'), hf_x=t, hf_y=wrpll_sim.gtx+1, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='main'), hf_x=t, hf_y=wrpll_sim.main, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='helper'), hf_x=t, hf_y=wrpll_sim.helper-1, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='gtx'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.gtx+1, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='main'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.main, row=4, col=1)\n",
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"fig.add_trace(go.Scattergl(name='helper'), hf_x=wrpll_sim.time, hf_y=wrpll_sim.helper-1, row=4, col=1)\n",
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"\n",
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"\n",
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"fig.update_layout(\n",
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