"
]
},
"metadata": {},
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],
"source": [
"time = rec[\"amp\"].time\n",
"\n",
"sample_bl_shift = sa.gen_bl_shift(time, 1000)\n",
"sample_spike = sa.gen_spike(time, 2000, 3)\n",
"\n",
"display(sample_bl_shift)\n",
"\n",
"fig, ax = p.subplots(1, 1, figsize=(12,2))\n",
"ax.plot(time, sample_bl_shift, \"r-\", label=\"bl_shift\")\n",
"ax.plot(time, sample_spike, \"g-\", label=\"spike\")\n",
"ax.set_xlabel('Time / s')\n",
"ax.set_ylabel('Amp')\n",
"ax.legend()\n",
"\n",
"p.tight_layout()\n",
"p.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Controlling Artifact Timing\n",
"\n",
"Artifacts can be placed using a timing dataframe with columns onset_time, duration, trial_type, value, and channel (extends stim dataframe).\n",
"\n",
"We can use the function add_event_timing to create and modify timing dataframes. The function allows precise control over each event.\n",
"\n",
"The function sel_chans_by_opt allows us to select a list of channels by way of a list of optodes. This reflects the fact that motion artifacts usually stem from the motion of a specific optode or set of optodes, which in turn affects all related channels.\n",
"\n",
"We can also use the functions random_events_num and random_events_perc to add random events to the dataframe—specifying either the number of events or the percentage of the timeseries duration, respectively."
]
},
{
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