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Analysis of PELT behaviours and DecayClamping effects
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"source": "%matplotlib inline\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom devlib.utils.misc import memoized\nfrom collections import namedtuple",
"execution_count": 1,
"outputs": []
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"source": "This Notebook provides a simple PELT model which allows to simulate the\nbehaviours of the PELT signal for a single task.\n\nDespite being of a bare simplicity it's still a useful tool to show and study\nhow PELT describes a task depending on its profile (period and duty-cycle) as\nwell as some specific PELT attributes (half-life, intial values).\n\nThe current PELT model can be easily extended to evaluate experimental features\nsuch as (for example) the capping of the decay value. This feature is already\nsupported by the current implementation.\n\nThe rest of this notebook provides:\n- a simple [task model](#Task-Model) which allows to define period tasks\n- a short [summary of the main PELT features](#PELT-Theory)\n- a [PELT model](#PELT-Model) implementation\n- a set of [PELT Behavior Examples](#PELT-Behavior-Examples)\n- a presentation of the [capping effect on small tasks which sleep long times](#Capping-effect-on-small-tasks-which-sleep-long-times)"
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"cell_type": "markdown",
"source": "# Task Model"
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"frozen": false,
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"source": "class Task(object):\n \n pelt_sample_us = 1024\n pelt_max = 1024\n \n def __init__(self, period_ps, run_ps, start_ps=0):\n \"\"\"\n A task which activations are specified in terms\n of PELT samples.\n A PELT sample is 1.024 ms long, this is the time\n interval used by the Linux scheduler to update a\n PELT signal.\n \n :param start_ps: task start time\n :type start_ps: int\n \n :param period_ps: the period of each activation\n :type period_ps: int\n \n :param run_ps: the duration of each activation\n :type run_ps: int \n \"\"\"\n self.start_us = Task.pelt_sample_us * start_ps\n self.period_us = Task.pelt_sample_us * period_ps\n self.run_us = Task.pelt_sample_us * run_ps\n \n self.idle_us = self.period_us - self.run_us\n self.duty_cycle_pct = int(100. * run_ps / period_ps)\n self.util_avg = int(float(Task.pelt_max) * self.duty_cycle_pct / 100)\n \n def running(self, time_ms):\n #\n # +----+ +----+\n # | | | |\n # +----+ +----\n # -+----+----+----+------->\n # 1 2 3 4 PELT Samples\n # R S R S Task Status\n # ^ ^ ^ ^ Task running sampling time\n #\n #\n # NOTE: Since we sample exactly at PELT samples intervals, the exact time\n # when a task starts to run we consider it as if if was running for the entire\n # previous PELT interval. To fix this, by exploting the knowledge that a task\n # is always configured using an integer number of run/speep PELT samples, we can\n # look at the 1us before time wrt the specified sample time.\n # The 1us before time is always represeting the exact status of the task\n # in that period. This is why we remove 1 in the following computation:\n period_delta_us = (float(time_ms) * 1e3 - 1) % self.period_us\n if period_delta_us <= self.run_us:\n return True\n return False\n \n def __str__(self):\n return \"Task(start: {:.3f} [ms], period: {:.3f} [ms], run: {:.3f} [ms], \"\\\n \"duty_cycle: {:.3f} [%], util_avg: {:d})\"\\\n .format(self.start_us/1e3, self.period_us/1e3,\n self.run_us/1e3, self.duty_cycle_pct,\n self.util_avg)\n\nclass PeriodicTask(Task):\n \"\"\"\n A periodic task which is specified by period and duty-cycle.\n \n :see also: Task\n \"\"\"\n def __init__(self, period_ps, duty_cycle_pct, start_ps=0):\n run_ps = period_ps * float(duty_cycle_pct) / 100\n super(PeriodicTask, self).__init__(period_ps, run_ps, start_ps)\n\nclass AndroidTask(Task):\n \"\"\"\n An \"android\" task which is specified by run-time over a (default) period\n of 16ms.\n \n :see also: Task\n \"\"\"\n def __init__(self, run_ps, start_ps=0, period_ps=16):\n super(AndroidTask, self).__init__(period_ps, run_ps, start_ps)\n",
"execution_count": 2,
"outputs": []
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"cell_type": "markdown",
"source": "## Task Model Validation"
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"read_only": false
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"cell_type": "code",
"source": "t1 = Task(start_ps=1, period_ps=16, run_ps=4)\nprint t1\n\nt2 = PeriodicTask(start_ps=1, period_ps=16, duty_cycle_pct=1)\nprint t2\n\nt3 = AndroidTask(run_ps=4)\nprint t3\n\nrun = []\nfor t_ps in xrange(1,16):\n run.append(t3.running(t_ps * 1.024))\nprint run",
"execution_count": 3,
"outputs": [
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"text": "Task(start: 1.024 [ms], period: 16.384 [ms], run: 4.096 [ms], duty_cycle: 25.000 [%], util_avg: 256)\nTask(start: 1.024 [ms], period: 16.384 [ms], run: 0.164 [ms], duty_cycle: 1.000 [%], util_avg: 10)\nTask(start: 0.000 [ms], period: 16.384 [ms], run: 4.096 [ms], duty_cycle: 25.000 [%], util_avg: 256)\n[True, True, True, True, False, False, False, False, False, False, False, False, False, False, False]\n",
"name": "stdout"
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"metadata": {
"run_control": {
"frozen": false,
"read_only": false
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"cell_type": "markdown",
"source": "# PELT Theory"
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"run_control": {
"frozen": false,
"read_only": false
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"cell_type": "markdown",
"source": "Infinite geometric series\n-----------------------------\nThe series:\n\n$$\\sum_{n=0}^{\\infty} = uy^n$$\n\nconverges to:\n\n$$\\frac{u}{1 - y}\\ \\quad for \\quad \\mid y \\mid < 1$$\n\nPELT Properties\n------------------\n\nPELT defined the value of $y$ such that $y^{half\\_life} = 0.5$, thus:\n $$y = 0.5^{1/half\\_life}$$\n\nPELT defines the covergence value to be 1024, thus:\n \n $$1024 = \\frac{u}{1 - y} \\implies u = 1024 \\cdot (1 - y)$$\n \nThis means that:\n $$u = 1024 \\cdot (1 - 0.5^{1/half\\_life})$$\n \nGiven a periodic task, where:\n\n - $R$ is the number of *running intervals*\n - $P$ is the duration of the *task period*, always espressed in PELT intervals\n \nthe PELT signal for such a task is granted to be stable and it oscillate\nbetween the two values defined by the following closed form fomulaes:\n\n $$\\frac{1-y^R}{1-y^P} \\lt PELT \\lt y^i * \\frac{1-y^R}{1-y^P}$$\n "
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"cell_type": "markdown",
"source": "# PELT Model"
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"frozen": false,
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"source": "from collections import namedtuple\n\nPELTStats = namedtuple('PELTStats', [\n 'util_avg', 'util_init', 'half_life',\n 'tmin', 'tmax', 'min', 'max', 'avg', 'std',\n 'err', 'err_pct'\n])\n\nclass PELT(object):\n \"\"\"\n A simple PELT simulator which allows to compute and plot the PELT signal\n for a specifed (periodic) task. The simulator can be configured using the\n main settings exposed by PELT: the half-life of a task and its intial\n value. The capping of the decay is an experimental features which is also\n supported by the current model.\n \n The model is pretty simple and works by generating a new value at each\n PELT sample interval (_sample_us) specified in [us] (1024 by default).\n \n The model allows to compute statistics on the generated signal, over a\n specified time frame. It provides also an embedded support to plot the\n generated signal along with a set of expected thresholds and values\n (e.g. half-life, min/max stability values)\n \"\"\"\n\n _sample_us = 1024\n _signal_max = 1024\n \n def __init__(self, task, init_util=0, half_life_ms=32,\n decay_cap_ms=None, verbose=False):\n \"\"\"\n Initialize the PELT simulator for a specified task.\n \n :param task: the task we want to simulate the PELT signal for\n :type task: Task\n \n :param init_util: the initial utilization for the task\n :type init_util: int\n \n :param half_life_ms: the [ms] interval required by a signal to\n increased/decreased by half of its range\n :type half_life_ms: int\n \n :param decay_cap_ms: the [ms] interval after which we do not decay\n further a signal\n :type decay_cap_ms: int\n \n \"\"\"\n self.task = task\n self.init_util = init_util\n self.half_life_ms = half_life_ms\n self.decay_cap_ms = decay_cap_ms\n \n self.geom_y = pow(0.5, 1./half_life_ms)\n self.geom_u = float(PELT._signal_max) * (1. - self.geom_y)\n\n self.df = None\n\n self._verbose = verbose\n if not verbose:\n return\n\n print \"PELT Configured with :\"\n print \" initial utilization : \", self.init_util\n print \" half life (HL) [ms] : \", self.half_life_ms\n print \" decay capping @ [ms] : \", self.decay_cap_ms\n print \" y = 0.5^(1/HL) : {:6.3f}\".format(self.geom_y)\n print \" u = {:5d}*(1 - y) : {:6.3f}\".format(PELT._signal_max, self.geom_u)\n print \"Task configured with :\"\n print \" run_time [us] : \", self.task.run_us\n print \" period [us] : \", self.task.period_us\n print \" duty_cycle [%] : \", self.task.duty_cycle_pct\n (min_val, max_val) = self.stable_range()\n print \" stable range : ({:.1f}, {:.1f})\".format(min_val, max_val)\n \n def geom_sum(self, u_1, active_us=0):\n \"\"\"\n Geometric add the specified value to the u_1 series.\n \n NOTE: the current implementation assume that the task was active\n (active_us != 0) or sleeping (active_us == 0) a full PELT\n sampling interval (by default 1024[us])\n \"\"\"\n # Decay previous samples\n u_1 *= self.geom_y\n # Add current sample (if task was active)\n if active_us:\n u_1 += self.geom_u\n return u_1\n\n def signal(self, start_s=0, end_s=10):\n \"\"\"\n Compute the PELT signal for the specified interval.\n \n :param start_s: the start time in [s]\n :type start_s: float\n \n :param end_s: the end time in [s]\n :type end_s: float\n \"\"\"\n # Computed PELT samples\n samples = []\n # Decay capping support\n running_prev = True\n capping = None\n \n # Intervals (in PELT samples) for the signal to compute\n start_us = int(1e6 * PELT._sample_us/1e3 * start_s)\n end_us = int(1e6 * PELT._sample_us/1e3 * end_s)\n if self._verbose:\n print \"Signal Range : ({}:{}) [us]\".format(start_us, end_us)\n \n # Add initial value to the generated signal\n util = self.init_util\n t_us = start_us\n samples.append((float(t_us)/1e6, 0, False, util))\n \n # Compute following PELT samples\n t_us = start_us + PELT._sample_us\n while t_us <= end_us:\n \n # Check if the task was running in the current PELT sample\n running = self.task.running(float(t_us/1e3))\n\n # Keep track of sleep start and decay capping time\n if self.decay_cap_ms and running_prev and not running:\n capping = t_us + (self.decay_cap_ms * 1e3)\n #print \"Set capping @{}\".format(t_capping)\n\n # Assume the task was running for all the current PELT sample\n active_us = PELT._sample_us if running else 0\n\n # Always update utilization:\n # - when the task is running\n # - when is not running and we are not capping the decay\n if running or not self.decay_cap_ms:\n util = self.geom_sum(util, active_us)\n\n # Update decay only up to the capping point\n elif capping and t_us < capping:\n util = self.geom_sum(util, active_us)\n\n # Keep tracking the decay capped value\n #else:\n # print \"{}: capped @{} (since {})\".format(t_us, util, capping)\n\n # Append PELT samples\n samples.append((float(t_us)/1e6, t_us/PELT._sample_us, running, util))\n \n # Prepare for next sample computation\n running_prev = running\n t_us += PELT._sample_us\n\n # Create DataFrame from computed samples\n self.df = pd.DataFrame(samples, columns=['Time', 'PELT_Interval', 'Running', 'util_avg'])\n self.df.set_index('Time', inplace=True)\n \n return self.df\n\n def stats(self, start_s=0, end_s=None):\n \"\"\"\n Compute the stats over a pre-computed PELT signal, considering only\n the specified portion of the signal.\n \n :param start_s: the start of signal portion to build the stats for\n :type start_s: float\n \n :param end_s: the end of signal portion to build the stats for\n :type end_s: float\n \"\"\"\n \n # Intervals (in PELT samples) for the statistics to compute\n start_s *= float(self._sample_us)/1e3\n start_s = max(start_s, self.df.index.min())\n if end_s:\n end_s *= float(self._sample_us)/1e3\n end_s = min(end_s, self.df.index.max())\n else:\n end_s = self.df.index.max()\n \n df = self.df[start_s:end_s]\n if self._verbose:\n print \"Stats Range : ({:.6f}:{:.6f}) [s]\".format(start_s, end_s)\n\n # Compute stats\n _util_avg = self.task.util_avg\n _util_init = self.init_util\n _half_life = self.half_life_ms\n _tmin = start_s\n _tmax = end_s\n _min = df.util_avg.min()\n _max = df.util_avg.max()\n _avg = df.util_avg.mean()\n _std = df.util_avg.std()\n _err = _avg - self.task.util_avg\n _err_pct = None\n if self.task.util_avg:\n _err_pct = 100 * _err / self.task.util_avg\n\n return PELTStats(\n _util_avg, _util_init, _half_life,\n _tmin, _tmax, _min, _max,\n _avg, _std, _err, _err_pct)\n \n def plot(self, start_s=0, end_s=10, stats_start_s=0, stats_end_s=None):\n \"\"\"\n Plot a PELT signal for the specified interval and report stats for a\n possibly different portion of the signal.\n \n NOTE: the portion of signal used for statistics is highlighted in\n yellow in the generated plot.\n \n :param start_s: the start time in [s]\n :type start_s: float\n \n :param end_s: the end time in [s]\n :type end_s: float\n \n :param stats_start_s: the start of signal portion to build the stats for\n :type stats_start_s: float\n\n :param stats_end_s: the end of signal portion to build the stats for\n :type stats_end_s: float \n \"\"\"\n\n # Generate PELT signal for the task\n self.signal(start_s, end_s)\n pelt_stats = self.stats(stats_start_s, stats_end_s)\n\n # Plot PELT signal and half life marker\n fig, axes = plt.subplots(1, 1, figsize=(16, 8))\n \n # Plot simulated PELT signal\n self.df.util_avg.plot(ax=axes)\n \n # Plot HALF-LIFE vertical marker\n hl_s = float(self.half_life_ms) * PELT._sample_us/1e3\n hl_s /= 1e3\n axes.vlines(hl_s, 0, 1024, colors=['r'], linestyle='--')\n\n # Plot expected MIN/MAX stable variation markers\n (util_min, util_max) = self.stable_range()\n axes.hlines(util_min, 0, 1024, colors=['r'], linestyle='--')\n axes.hlines(util_max, 0, 1024, colors=['r'], linestyle='--')\n\n # Plot AVERAGE horizontal marker\n axes.hlines(pelt_stats.avg, 0, 1024, colors=['c'], linestyle='--')\n # Plot horizontal +-1% EXPECTED UTILIZATION bands\n axes.axhspan(self.task.util_avg - 10,\n self.task.util_avg + 10,\n facecolor='g', alpha=0.1)\n \n # Plot STATS vertical band\n axes.axvspan(pelt_stats.tmin, pelt_stats.tmax,\n facecolor='y', alpha=0.1)\n \n # Plot title with stats\n err_str = \"{:.1f} [%]\".format(pelt_stats.err_pct) if pelt_stats.err_pct else \"NaN\"\n axes.set_title('Task uitl: {}, run {} [ms] every {} [ms], avg: {:.1f}, '\\\n 'err: {}, min: {:.1f}, max: {:.1f}'.format(\n int(self.task.util_avg),\n int(float(self.task.run_us)/1e3),\n int(float(self.task.period_us)/1e3),\n pelt_stats.avg, err_str,\n pelt_stats.min, pelt_stats.max\n ))\n\n return pelt\n\n def __str__(self):\n \"\"\"\n Return a textual description of the PELT signal for the specified task\n \"\"\"\n print \"Task configuration:\"\n print \" \", self.task\n\n print \"PELT engine configuration:\"\n print \" half-life: {} ms\".format(self.half_life_ms)\n print \" init_util: {}\".format(self.init_util)\n\n # Report stats\n print \"Utilization signal:\"\n print \" [min,max]: {:5.1f}, {:5.1f}\".format(self.min, self.max)\n print \" [avg,std]: {:5.1f} (+- {:5.1f})\".format(self.avg, self.std)\n print \" expected: {:5.1f}\".format(self.task.util)\n print \" error: {:5.1f} ({:.1f} %)\".format(self.err, self.err_pct)\n return \"\"\n \n def stable_range(self):\n \"\"\"\n Compute the stability ranges for the specified (periodic) task\n \n Here we use:\n - Decay factor: y = 0.5^(1/half_life)\n - Max stable value: (1-y^r) / (1-y^p)\n - Min stable value: y^i * (1-y^r) / (1-y^p)\n \"\"\"\n\n max_util = (1. - pow(self.geom_y, float(self.task.run_us)/PELT._signal_max))\n max_util /= (1. - pow(self.geom_y, float(self.task.period_us)/PELT._signal_max))\n \n min_util = (max_util * pow(self.geom_y, float(self.task.idle_us)/PELT._signal_max))\n\n return (PELT._signal_max * min_util, PELT._signal_max * max_util)",
"execution_count": 4,
"outputs": []
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"metadata": {},
"cell_type": "markdown",
"source": "# PELT Behavior Examples"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## PELT slow ramp-up"
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"metadata": {
"trusted": true
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"cell_type": "code",
"source": "t = Task(period_ps=1000, run_ps=1000)\npelt = PELT(t, init_util=0, half_life_ms=32, decay_cap_ms=None, verbose=True)\npelt.plot(end_s = 0.1);",
"execution_count": 8,
"outputs": [
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"output_type": "stream",
"text": "PELT Configured with :\n initial utilization : 0\n half life (HL) [ms] : 32\n decay capping @ [ms] : None\n y = 0.5^(1/HL) : 0.979\n u = 1024*(1 - y) : 21.942\nTask configured with :\n run_time [us] : 1024000\n period [us] : 1024000\n duty_cycle [%] : 100\n stable range : (1024.0, 1024.0)\nSignal Range : (0:102400) [us]\nStats Range : (0.000000:0.102400) [s]\n",
"name": "stdout"
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"output_type": "display_data",
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GtqYVpmlaYaoqC1JVmkM4ncgIrgAAAABGLRJxqmoJaXtdl7bXdWtbXZe21/u/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\nNtIxN5IkSZI+mMVVObN4TT3fuX8hMxdW85n/6cW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VLK6SJEmSpFSzuEqSJEmSUs3iKkmS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\nxhUAAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fbc37472910>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## PELT speed-up inducted oscillations"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "t = Task(period_ps=16, run_ps=4)\npelt = PELT(t, init_util=0, half_life_ms=8, decay_cap_ms=None, verbose=True)\npelt.plot(end_s = 0.1);",
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": "PELT Configured with :\n initial utilization : 0\n half life (HL) [ms] : 8\n decay capping @ [ms] : None\n y = 0.5^(1/HL) : 0.917\n u = 1024*(1 - y) : 84.988\nTask configured with :\n run_time [us] : 4096\n period [us] : 16384\n duty_cycle [%] : 25\n stable range : (141.4, 399.9)\nSignal Range : (0:102400) [us]\nStats Range : (0.000000:0.102400) [s]\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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QCFxjFHlcAQAAAMQLAtcYRR5XAAAAAPGCwBUAAAAAEGgdClzNLMPMnjKzT83s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\nuIqIiIiIiIhP+/8XrQlG6qeIXwAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fbc39be3c10>"
},
"metadata": {}
}
]
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"cell_type": "markdown",
"source": "# Capping effect on small tasks which sleep long times"
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false,
"collapsed": true
},
"cell_type": "code",
"source": "t300 = Task(start_ps=0, period_ps=300, run_ps=30)",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "print \"Task without decay capping\"\npelt = PELT(t300, init_util=0, half_life_ms=32, decay_cap_ms=None, verbose=True)\npelt.plot(end_s=1.2, stats_start_s=0.3);",
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Task without decay capping\nPELT Configured with :\n initial utilization : 0\n half life (HL) [ms] : 32\n decay capping @ [ms] : None\n y = 0.5^(1/HL) : 0.979\n u = 1024*(1 - y) : 21.942\nTask configured with :\n run_time [us] : 30720\n period [us] : 307200\n duty_cycle [%] : 10\n stable range : (1.4, 490.1)\nSignal Range : (0:1228800) [us]\nStats Range : (0.307200:1.228800) [s]\n"
},
{
"output_type": "display_data",
"data": {
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jxxUAAAAAEGrRDVzJ4woAAAAAeSG6gSt5XAEAAAAgL0Q3cAUAAAAA5IW0Alcz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BAAAA\n20lEQVRLPZ4DjFFKdfxOnlIqV2sdSGukQgghRAZI4iqEEEJkXrjL40SXnxN8fq22ADO01qF0BiaE\nEEIYgZQKCyGEEObwKp+XDaOUmpjBWIQQQoi0ksRVCCGEMIdvA1OVUp8opVaRnBMrhBBC9AmyHY4Q\nQgghhBBCCEOTEVchhBBCCCGEEIYmiasQQgghhBBCCEOTxFUIIYQQQgghhKFJ4iqEEEIIIYQQwtAk\ncRVCCCGEEEIIYWiSuAohhBBCCCGEMDRJXIUQQgghhBBCGJokrkIIIYQQQgghDO3/A/ftZSCJChtS\nAAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f5164e84850>"
},
"metadata": {}
}
]
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "print \"Android task with decay capping @ 16ms\"\npelt = PELT(t300, init_util=0, half_life_ms=32, decay_cap_ms=16, verbose=True)\npelt.plot(end_s=1.2, stats_start_s=0.3);",
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Android task with decay capping @ 16ms\nPELT Configured with :\n initial utilization : 0\n half life (HL) [ms] : 32\n decay capping @ [ms] : 16\n y = 0.5^(1/HL) : 0.979\n u = 1024*(1 - y) : 21.942\nTask configured with :\n run_time [us] : 30720\n period [us] : 307200\n duty_cycle [%] : 10\n stable range : (1.4, 490.1)\nSignal Range : (0:1228800) [us]\nStats Range : (0.307200:1.228800) [s]\n"
},
{
"output_type": "display_data",
"data": {
"image/png": 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BH7Xrz79zKNvNAIrCPXsHVV5qeubW1ZKkptpySdKxIXpdgVQUb+AKAEARGJ2I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\nCgAAwKD9H2DR8vDUMkMRAAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f5164e84790>"
},
"metadata": {}
}
]
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "print \"Android task with decay capping @ 32ms\"\npelt = PELT(t300, init_util=0, half_life_ms=32, decay_cap_ms=32, verbose=True)\npelt.plot(end_s=1.2, stats_start_s=0.3);",
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Android task with decay capping @ 32ms\nPELT Configured with :\n initial utilization : 0\n half life (HL) [ms] : 32\n decay capping @ [ms] : 32\n y = 0.5^(1/HL) : 0.979\n u = 1024*(1 - y) : 21.942\nTask configured with :\n run_time [us] : 30720\n period [us] : 307200\n duty_cycle [%] : 10\n stable range : (1.4, 490.1)\nSignal Range : (0:1228800) [us]\nStats Range : (0.307200:1.228800) [s]\n"
},
{
"output_type": "display_data",
"data": {
"image/png": 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AAFDQWMcVAAAAAFDQGHEFAAAAABS06AaurOMKAAAAAEUhuoEr67gCAAAAQFGI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\n8nK5nrRLAAAAQJVrbl73G3ffdrLHJT3jelDSqfO+X1+47wX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"text/plain": "<matplotlib.figure.Figure at 0x7f5164e7b5d0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "print \"Android task with decay capping @ 64ms\"\npelt = PELT(t300, init_util=0, half_life_ms=32, decay_cap_ms=64, verbose=True)\npelt.plot(end_s=2, stats_start_s=0.3);",
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Android task with decay capping @ 64ms\nPELT Configured with :\n initial utilization : 0\n half life (HL) [ms] : 32\n decay capping @ [ms] : 64\n y = 0.5^(1/HL) : 0.979\n u = 1024*(1 - y) : 21.942\nTask configured with :\n run_time [us] : 30720\n period [us] : 307200\n duty_cycle [%] : 10\n stable range : (1.4, 490.1)\nSignal Range : (0:2048000) [us]\nStats Range : (0.307200:2.048000) [s]\n"
},
{
"output_type": "display_data",
"data": {
"image/png": 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VPK4AAAAAEI04A1fyuAIAAABANOIMXAEAAAAA0SBwBQAAAAC0NAJXAAAAAEBL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\nGFxbbuK7dmk33ak439d9JBQurmUEuGllhmTcqH0RElc7QN857LU7v99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"text/plain": "<matplotlib.figure.Figure at 0x7f5164ab7110>"
},
"metadata": {}
}
]
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"cell_type": "markdown",
"source": "## Analysis of capping effects on task estimation"
},
{
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "# List of dacay capping (in [ms]) to compare\ncap_ms = [32,64, 128]",
"execution_count": 27,
"outputs": []
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false,
"collapsed": true
},
"cell_type": "code",
"source": "import matplotlib.gridspec as gridspec\n\nclass Variations(object):\n \n def __init__(self, period_ps, run_ps, cap_ms):\n self.period_ps = period_ps\n self.run_ps = run_ps\n self.cap_ms = cap_ms\n\n def compute(self):\n\n # Compute reference (i.e. no capping at all)\n task = Task(start_ps=0, period_ps=self.period_ps, run_ps=self.run_ps)\n pelt = PELT(task, init_util=0, half_life_ms=32, decay_cap_ms=0)\n pelt.signal(end_s=2)\n stats_base = pelt.stats(start_s=1, end_s=2)\n\n # Normalize all other stats wrt the no capping case\n def normalize(ms, base, curr):\n _avg = 100. * (curr.avg - base.avg) / base.avg\n _std = 100. * (curr.std - base.std) / base.std\n _max = 100. * (curr.max - base.max) / base.max\n return ms, _avg, _std, _max\n\n # Compute capping for different [ms] thresholds\n data = []\n for ms in cap_ms:\n pelt = PELT(t300, init_util=0, half_life_ms=32, decay_cap_ms=ms)\n pelt.signal(end_s=2)\n stats_curr = pelt.stats(start_s=1, end_s=2)\n data.append(normalize(ms, stats_base, stats_curr))\n\n self.stats_df = pd.DataFrame(data, columns=['capping_ms', 'avg_pct', 'std_pct', 'max_pct'])\n self.stats_df.set_index('capping_ms', inplace=True)\n\n return self.stats_df\n\n def plot(self):\n\n # Plot stats variations\n gs = gridspec.GridSpec(1, 3, height_ratios=[2, 1, 1])\n\n ax = plt.subplot(gs[0, 0])\n ax = self.stats_df.avg_pct.plot(axes=ax, kind='bar', figsize=(16,8));\n ax.set_ylabel(\"Avg wrt no capping [%]\");\n ax.set_xlabel(\"Decay capping [ms]\");\n\n ax = plt.subplot(gs[0, 1])\n ax = self.stats_df.std_pct.plot(axes=ax, kind='bar', figsize=(16,8));\n ax.set_ylabel(\"Std wrt no capping [%]\");\n ax.set_xlabel(\"Decay capping [ms]\");\n\n ax = plt.subplot(gs[0, 2])\n ax = self.stats_df.max_pct.plot(axes=ax, kind='bar', figsize=(16,8));\n ax.set_ylabel(\"Max wrt no capping [%]\");\n ax.set_xlabel(\"Decay capping [ms]\");",
"execution_count": 28,
"outputs": []
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "task = Task(start_ps=0, period_ps=100, run_ps=60)\npelt = PELT(task, init_util=0, half_life_ms=32, decay_cap_ms=0, verbose=True)\npelt.plot(end_s=0.5, stats_start_s=0);",
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "PELT Configured with :\n initial utilization : 0\n half life (HL) [ms] : 32\n decay capping @ [ms] : 0\n y = 0.5^(1/HL) : 0.979\n u = 1024*(1 - y) : 21.942\nTask configured with :\n run_time [us] : 61440\n period [us] : 102400\n duty_cycle [%] : 60\n stable range : (353.7, 841.3)\nSignal Range : (0:512000) [us]\nStats Range : (0.000000:0.512000) [s]\n"
},
{
"output_type": "display_data",
"data": {
"image/png": 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kSZIkSZJKM6Ao4sln6Dd6W+LJZ7q7KJIkSZIkSerhDCgK5jbQ58ln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\n/okAAAAAMA8jXSgePJhs397Lhg2WOwMAAADAXIx0o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"text/plain": "<matplotlib.figure.Figure at 0x7f86c09fbc50>"
},
"metadata": {}
}
]
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "v = Variations(300, 1, [32,64,128])\nv.compute()\nv.plot()",
"execution_count": 167,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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Gnaxbt+4nyzMzM8zMzIxr1+rU2P4JdMwGbx/Mzs4yOzu7lE3fRTNo1cCg08/3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\n0rJt9h3eqpoFZpNsRTMf27O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"text/plain": "<matplotlib.figure.Figure at 0x7fa280588250>"
},
"metadata": {}
}
]
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "# Plot stats variations\nimport matplotlib.gridspec as gridspec\ngs = gridspec.GridSpec(1, 3, height_ratios=[2, 1, 1])\n\nax = plt.subplot(gs[0, 0])\nax = stats_df.avg_pct.plot(axes=ax, kind='bar', figsize=(16,8));\nax.set_ylabel(\"Avg wrt no capping [%]\");\nax.set_xlabel(\"Decay capping [ms]\");\n\nax = plt.subplot(gs[0, 1])\nax = stats_df.std_pct.plot(axes=ax, kind='bar', figsize=(16,8));\nax.set_ylabel(\"Std wrt no capping [%]\");\nax.set_xlabel(\"Decay capping [ms]\");\n\nax = plt.subplot(gs[0, 2])\nax = stats_df.max_pct.plot(axes=ax, kind='bar', figsize=(16,8));\nax.set_ylabel(\"Max wrt no capping [%]\");\nax.set_xlabel(\"Decay capping [ms]\");\n",
"execution_count": 145,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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FjklyVvv3uiU+KkeSJEnj5cPAj4AXAC8E7gQ+MtKKJGmOXh6V8yGa24tPpXnu\n18uAe6vqlYMvr2s9jo1wXIQ0Sx8mbNqS5pmGv94umgb+T1Xd04fyFluTWQeYd9JsA3xUzgMeD9aP\nR4b1cFyzDjDrpNmWMub1gKrap+P9RUmu6V9pkjRyJ9Pk4V+zoZPuZGAknXSSNAI/S/K0qroU7p9B\n+KcjrkmSZuml8XpvksfOPDIiyS/SPN5BkpYLO+kkrXR/CJzWMc71djY8o1WSxkIvjdc3Av+S5Ob2\n/SrgiIFVJEnDZyedpBWtqq4G9kmyffv+RyMuSZIeYMHGa1VdlGRP4HE0gwf+vap+PvDKJGl47KST\ntKIl2Qp4Pk3+bZ520GhVvW2khUlSh16f87o/sHu7/r7tANrTBleWJA2PnXSSxKeAO4CvAHeNuBZJ\n6mrBxmuSjwGPAa4G7uv4yMarpOXETjpJK9kjquq/jboISdqYXq68PhnYayDzmEvSGLCTTpL4UpJ9\nqsrJ6iSNrV4ar18DdgW+PeBaJGlU7KSTtNI9DTiiHfs/M2yi5szELkkj1Uvj9ReANUkuZ3aYHTq4\nsiRpqOykk7TSPXvUBUjSQnppvK7ussyrE5KWEzvpJK1oVbV21DVI0kJ6eVTO9BDqkKRRWt1lmZ10\nkiRJY6TXR+VI0rJlJ50kSdL422zUBUiSJGm0kryrl2WSNEoLNl6THNPLMkmSJE2sZ3VZ9t+HXoUk\nbUQvV15f3mXZEX2uQ5JGxk46SStVktckuRZ4XJJrO/7WAj7zVdJYyXyPNUzyEuClNM/9urTjo+2A\n+6rqGYMvr2tdA3kUYxImZ36W4OMopQ2SUFVZwvZXVdV+c5ZdXVX7LmGf7wGeA9wNfAM4oqp+mGQV\ncD1wQ7vql6vqqC7bm3WAeSfNttS867K/HYCHAu8EjgVm9n1nVX2/x318GPgt4LtVtXe7bDXwSuB7\n7WrHVdV5XbY16wCzTpptvqzb2IRNXwK+A+wE/DkbwuxH2BMnaRno6KTbPcm5HR9tB/T0o20jzgfe\nVFXrk5wIHEfzwxDgprmNZUkahbZT7SfA/lX1n4vczUeADwCnde4aOKmqTlpqjZI0Y97Ga1X9Z5Jv\nAT+vqkuGWJMkDcvAOumq6oKOt5cBz1/K/iRpUKrq3iQ3JHn0YhqwVXVpe1fJXH27QixJsMCjctow\nuy/JQ6rqjmEVJUnDMMROulcAZ3S83z3JVcAPgbdU1RcGeGxJ6sWOwHVJLgd+0i6rqjp0Cft8XZLf\nB64A3uAFcQwCAAAbFUlEQVRvyeWhuSV7snhL9vLRy3NefwJcm+R84KftsqqqowdXliQNx1I66ZJc\nAOzS5aPjq+rcdp03A3dX1entZ98Gdquq25PsD5yd5AlVdefcnaxevfr+11NTU0xNTW1KeZKWgenp\naaanp4dxqLfwwCulS/nFfzLwtvb124H3Akd2W9Gsm0ST1BicvMb2StRr1s07YdP9KyQv77K4qurU\nRVW2RA7sBwf1S7P1YcKmc4D9aMap9q2Trs3PVwHPqKq75lnnYporElfOWW7WAeadNFu/J2zq2O8r\ngUuq6sZFbr8KOHdmwqZN+MysAyYp6zy3GobFTNg04xbgi1X1s/6XJUlj4R/bv05L+n+6JIcAbwSe\n3tlwTbITcHtV3ZfkMcAewH8s5ViS1AePAv4mye40t/l+Hri0qq5ezM6S7FpV32nfPg+4tj9lSlrJ\nernyehrwFOB2miD7PPCFqrp98OV1rcceOnuQpFn6cOX1N+lzJ12SG4EtgR+0i75cVUcleT7wp8A9\nwHrgT6rqM122N+sA806abVBXXjv2vzXwB8AfAw+vqs172OYM4Ok0k9+tA04ApoB9aQLnZuDVVbWu\ny7ZmHTBJWee51TDMl3ULNl47dvBw4HfZEGa9XLXtO0MO/BJKs/Wh8TpWnXRtTWYdYN5Jsw3wtuG3\nAr8GbAtcDVxKk4Pf7vex5hzXrAMmKes8txqGRTdek7wMOAjYh+ZB01+gCbMv9XDQbg+t3hH4BPBo\nYC3wwplJUpIcRzMr533A0VV1fpd9GnJ+CaVZ+vVjblw66dpazDrAvJNmG2Dj9SqaO0I+Q9OB96Wq\n+nm/j9PluGYdMElZ57nVMCyl8fp94Bs0s8ZNV9XNm3DQpwE/Bk7raLy+G7itqt6d5E3AQ6vq2CR7\nAacDBwCPAC4E9qyq9XP2acj5JZRm6cOV10V30g2KWTfDvJM6DfK24STbA08Fnga8AFhXVQcN4lgd\nxzTrgEnKOs+thmEpEzbtBDyBJsj+LMljga9X1e8ttOE8D60+lGZcBMCpwDRwLHAYcEZV3QOsTXIT\ncCDwrz3UqDHm88A0Ad7HIjvpJGk5SLI3zW+9Xwd+mWbCzs+PtChJmqOXxut2NDPQPRpYBTyEZpKR\nxdq5Y8D+OmDn9vXDmd1QvYXmCqyWhUlqDE5eY1tLtuhOOklaJt5JM871/cC/tRcTJGms9NJ4/QLw\nRZpA+6uquqVfB6+qSrKxVk3Xz3yYtbSy9fog603Q7046SZooVfWcUdcgSQvpebbhRR9gzoOpk9wA\nTFXVrUl2BS6uqscnORagqk5s1zsPOKGqLpuzP8dGTNi9+5N1bmHSzq/6Mub1GjZ00n2+n510i2XW\nzfD7KHUa9KNyhs2smzE5Wee51TAsZcxrv50DHA68q/3n2R3LT09yEs3twnsAl4+gPkkrTFXtM+oa\nJEmStHEDbbx2PrQ6yTeBPwFOBM5MciTto3IAqmpNkjOBNcC9wFED6YqTJEnSLEm2qqq75izbqapu\nG1VNkjTXwG8b7jdvL4FJu/1hss4tTNr51fK7jQ7Mug38PkqdBvic12uBP6iqL7fvnw+cWFV79PtY\nc45r1gGTlHWeWw3Dom8bTvIBmv9CZzYu4Ec0M9F9qq9VSpIkaRReCnw4yTTN8K3/Chw80ookaY5e\nbhveCngc8A80DdjnAzcD+yQ5uKr+aID1SdLAtJ1zMzo76aCZEP3oIZckSSNRVdcmeQfwUeBO4Gnj\nMHmdJHXqpfG6D/DUqroXIMkHaR6fcxBw7QBrk6RB+0r7z18D9gI+QdOAfQFw3aiKkqRhS/Ih4LHA\n3sCewKeT/FVV/dVoK5OkDXppvD4E2Ba4o32/LbBjVd2b5K75N5Ok8VZVpwAkeQ1wUFXd074/maaT\nTpJWiq8Br2wHoN6c5FeAk0ZckyTN0kvj9d3AVUkuad8/HXhHkm2ACwdWmSQNz0OA7YHvt++3a5dJ\n0opQVX8x5/0PgSNHVI4kddXTbMNJHg4cSDMm7Iqq+tagC9tILc5KN2Gzpk3WuYVJO79a+uybSY4A\nVgMX09w2/HRg9cyV2VEw62b4fZQ6DXC24T2BdwBPoJnvBJqx/4/p97HmHNesAyYp6zy3Gob5sm7B\nxmuSc4EzgE9V1U8GVF/PDDmYtC/hZJ1bmLTzq/78mEuyK/ArNP+xXl5V3+lLcYuvx6wD/D5Ksw2w\n8fpF4ASaW4V/GzgC2Lyq3trvY805rlkHTFLWeW41DPNl3WY9bPte4GnAmiSfTPK7SbZaaCNJmhRJ\nLqqq71TV2VX1qar6TpKLRl2XJA3R1lV1Ic2Fjf+sqtXAb424JkmaZcExr1U1DUwn2YLmeV+vAj5M\nMz5MkiZWkq2BBwO/kGTHjo+2p3nOoSStFHcl2Ry4KclrgW8D24y4JkmapZcJm2Z+4B0KvBDYHzh1\nkEVJ0pC8GjgGeDgbHpsDzTMOfTyEpJXkj2g6844G3k7TiXf4SCuSpDl6GfN6Js04sPOAvwcuqar1\nQ6htvnocGzFh9+5P1rmFSTu/6suETUdX1fv7XNPbaTr9imYW45dX1Tfbz44DXgHcBxxdVed32d6s\nA/w+SrMNaszrUiT5MM0txt+tqr3bZTvSPDv70cBa4IVVdUeXbc06YJKyznOrYVjKhE2HABdU1X3t\n+6cBL66q/zmQShdgyMGkfQkn69zCpJ1fLf7HXJIDgFtmJmdKcjjwfJofWqur6gdLqGm7qrqzff06\n4ElV9cokewGnAwfQ3Jp8IbDn3E5Bs26G30epU78br+3EnEUz0/pcVVWH9rCPpwE/Bk7raLy+G7it\nqt6d5E3AQ6vq2C7bmnXAJGWd51bDMF/W9TLm9bwk+yd5Cc1twzcDZw2gRkkatr8FngGQ5NeBE4HX\nAvu1n/3uYnc803BtbQvc1r4+DDijqu4B1ia5ieZRZP+62GNJ0hI8BbiF5skSl7XLZn4w9vSLv6ou\nTbJqzuJDaR47Bs1ws2ngAY1XSdoU8zZekzwOeAnwIuB7wD/QXKmdGk5pkjRwm3VcXX0R8DdVdRZw\nVpKvLnXnSf4MeBnwM5oGKjTjazsbqrfg5FCSRmdX4Jk0v/leAnyGpoPtuiXud+eqWte+XgfsvMT9\nSdJGr7xeD3wa+G9V9f8Akvx/Q6lKkoZj8yQPaq+C/ibwBx2fLXhnSpILgF26fHR8VZ1bVW8G3pzk\nWOB9NM9N7Kbr1Y3Vq1ff/3pqaoqpqamFSpK0zExPTzM9PT2w/VfVvcBngc8m+S80DdhLkqyuqr5M\nXFdVlWTeq7hmnaRes27eMa9JnksTYDOTNf0D8KGqWtW3KhfBsREwaffuT9a5hUk7v1rSmNc300wy\nchuwG/DkqlqfZA/glKp6ap/qexTwz1X1xLYhS1Wd2H52HnBCVV02ZxuzDvD7KM02iAmbkmxFk4Uv\nBlYB5wAfrqpvbcI+VgHndox5vQGYqqpbk+wKXFxVj++ynVkHTFLWeW41DPNl3WbzbVBVZ1fVi4An\nApcCr6d5FuLJSZ41uFIlaTiq6s+ANwAfAQ7qmDQpwOuWsu+2ATzjMOCq9vU5wIuTbJlkd2AP4PKl\nHEuSFivJR4Ev0Yz1f1tVHVB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AUVW1D/CnwNYAVfUlYFWSKWDzqlrTZdsH7LNd/xnAU9pe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\nAAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fa28088a890>"
},
"metadata": {}
}
]
},
{
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"trusted": false
},
"cell_type": "code",
"source": "stats_df",
"execution_count": 146,
"outputs": [
{
"execution_count": 146,
"output_type": "execute_result",
"data": {
"text/plain": " avg_pct std_pct max_pct\ncapping_ms \n32 274.165961 -39.553507 35.115546\n64 103.170748 -16.086327 15.218625\n128 15.962992 -1.489519 3.452069",
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>avg_pct</th>\n <th>std_pct</th>\n <th>max_pct</th>\n </tr>\n <tr>\n <th>capping_ms</th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>32</th>\n <td>274.165961</td>\n <td>-39.553507</td>\n <td>35.115546</td>\n </tr>\n <tr>\n <th>64</th>\n <td>103.170748</td>\n <td>-16.086327</td>\n <td>15.218625</td>\n </tr>\n <tr>\n <th>128</th>\n <td>15.962992</td>\n <td>-1.489519</td>\n <td>3.452069</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": false
},
"cell_type": "code",
"source": "t = Task(period_ps=10, run_ps=5);\nprint t",
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Task(start: 0.000 [ms], period: 10.240 [ms], run: 5.120 [ms], duty_cycle: 50.000 [%], util_avg: 512)\n"
}
]
}
],
"metadata": {
"kernelspec": {
"name": "python2",
"display_name": "Python 2",
"language": "python"
},
"toc": {
"threshold": 4,
"number_sections": true,
"toc_cell": false,
"toc_window_display": false,
"toc_section_display": "block",
"sideBar": true,
"navigate_menu": true,
"moveMenuLeft": true,
"colors": {
"hover_highlight": "#DAA520",
"selected_highlight": "#FFD700",
"running_highlight": "#FF0000"
},
"nav_menu": {
"width": "252px",
"height": "278px"
},
"toc_number_sections": true,
"toc_threshold": 6
},
"hide_input": false,
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12",
"file_extension": ".py",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
},
"gist": {
"id": "",
"data": {
"description": "Analysis of PELT behaviours and DecayClamping effects",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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