forked from M-Labs/artiq
371 lines
24 KiB
Plaintext
371 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"%pylab inline\n",
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"\n",
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"import os\n",
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"import logging\n",
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"import time\n",
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"import asyncio\n",
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"import datetime\n",
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"import glob\n",
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"from pprint import pprint\n",
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"\n",
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"import numpy as np\n",
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"np.set_printoptions(precision=3)\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn\n",
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"seaborn.set_style(\"whitegrid\")\n",
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"import pandas as pd\n",
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"import h5py\n",
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"\n",
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"from sipyco.pc_rpc import (Client, AsyncioClient,\n",
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" BestEffortClient, AutoTarget)\n",
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"from artiq.master.databases import DeviceDB\n",
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"from artiq.master.worker_db import DeviceManager"
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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": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# let's assume artiq_master and artiq_ctlmgr are already running\n",
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"# then move to a location where we have our artiq setup\n",
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"os.chdir(os.path.expanduser(\"~/work/nist/artiq/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": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# we can directly use the artiq controller infrastructure\n",
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"# and access any artiq device\n",
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"\n",
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"# we can have artiq prepare that connection for us:\n",
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"ddb = DeviceDB(\"device_db.py\")\n",
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"devmgr = DeviceManager(ddb)\n",
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"lda = devmgr.get(\"lda\")\n",
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"lda.set_attenuation(42)\n",
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"assert lda.get_attenuation() == 42\n",
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"\n",
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"# ... or we can wire it up ourselves if you know where it is\n",
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"assert ddb.get(\"lda\", resolve_alias=True)[\"host\"] == \"::1\"\n",
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"assert ddb.get(\"lda\", resolve_alias=True)[\"port\"] == 3253\n",
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"\n",
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"# there are different Client types tailored to different use cases:\n",
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"\n",
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"# synchronous\n",
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"lda = Client(\"::1\", 3253)\n",
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"assert lda.get_attenuation() == 42\n",
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"\n",
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"# asyncio\n",
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"lda = AsyncioClient()\n",
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"async def test_lda():\n",
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" await lda.connect_rpc(\"::1\", 3253, AutoTarget)\n",
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" return await lda.get_attenuation()\n",
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"assert asyncio.get_event_loop().run_until_complete(test_lda()) == 42\n",
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"\n",
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"# best effort\n",
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"lda = BestEffortClient(\"::1\", 3253, AutoTarget)\n",
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"assert lda.get_attenuation() == 42"
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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": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"current schedule\n",
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"{}\n",
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"experiments:\n",
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"['ex/',\n",
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" 'test_analyzer.py',\n",
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" 'notebook_test.py',\n",
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" 'speed_benchmark.py',\n",
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" 'histograms.py',\n",
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" 'arguments_demo.py',\n",
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" '.git/',\n",
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" '__pycache__/',\n",
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" 'flopping_f_simulation.py',\n",
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" 'test_crash.py',\n",
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" 'run_forever.py',\n",
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" 'transport.py',\n",
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" 'pdq2_simple.py']\n"
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]
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}
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],
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"source": [
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"# let's connect to the master\n",
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"\n",
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"schedule, exps, datasets = [\n",
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" Client(\"::1\", 3251, \"master_\" + i) for i in\n",
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" \"schedule experiment_db dataset_db\".split()]\n",
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"\n",
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"print(\"current schedule\")\n",
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"pprint(schedule.get_status())\n",
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"print(\"experiments:\")\n",
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"pprint(exps.list_directory(\"repository\"))"
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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": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"current schedule\n",
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"{4722: {'due_date': None,\n",
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" 'expid': {'arguments': {'F0': 1500, 'noise_amplitude': 0.3},\n",
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" 'class_name': 'FloppingF',\n",
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" 'file': 'repository/flopping_f_simulation.py',\n",
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" 'log_level': 30},\n",
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" 'flush': False,\n",
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" 'pipeline': 'main',\n",
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" 'priority': 0,\n",
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" 'repo_msg': None,\n",
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" 'status': 'preparing'}}\n"
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]
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}
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],
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"source": [
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"# we can submit experiments to be run\n",
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"\n",
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"expid = dict(\n",
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" file=\"repository/flopping_f_simulation.py\",\n",
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" class_name=\"FloppingF\",\n",
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" log_level=logging.WARNING,\n",
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" arguments=dict(\n",
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" F0=1500,\n",
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" noise_amplitude=.3,\n",
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" ),\n",
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")\n",
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"if not schedule.get_status():\n",
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" rid = schedule.submit(pipeline_name=\"main\", expid=expid,\n",
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" priority=0, due_date=None, flush=False)\n",
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"print(\"current schedule\")\n",
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"pprint(schedule.get_status())"
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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": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# wait for experiment to finish\n",
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"# this can be written nicer by subscribing and reacting to scheduler changes\n",
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"while rid in schedule.get_status():\n",
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" time.sleep(.1)"
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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": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"flopping_f: 1499.944285221012\n"
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]
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},
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{
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"data": {
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kiL1HG5A9IhmbvluKjCG9v3EPnbqCdT/ZhawMG5569HY8/eqnmHdnLlZ+e3D63b3+AB5Z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\nJKD/D7Ip5VrpmcohAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fc5187c8668>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# now that the experiment has completed, we can get the\n",
|
|
"# current value of the (live) dataset and plot it\n",
|
|
"# had we done this earlier, the dataset would have been incomplete\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"d = datasets.get(\"flopping_f_brightness\")\n",
|
|
"ax.plot(d)\n",
|
|
"print(\"flopping_f:\", datasets.get(\"flopping_freq\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# this is how you would clear all pipelines\n",
|
|
"for i in schedule.get_status():\n",
|
|
" schedule.delete(i)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"available datasets ['artiq_version', 'flopping_f_brightness']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# we can easily find and use the data that was saved as part\n",
|
|
"# of the experiment\n",
|
|
"\n",
|
|
"t = datetime.datetime.now()\n",
|
|
"f = os.path.join(\n",
|
|
" \"results\", t.strftime(\"%Y-%m-%d\"), #t.strftime(\"%H-%M\"),\n",
|
|
" \"*\", \"{:09d}-FloppingF.h5\".format(rid))\n",
|
|
"\n",
|
|
"# we would usually like to use pandas but our data does not have\n",
|
|
"# the metadata pandas want\n",
|
|
"#d = pd.HDFStore(glob.glob(f)[0])\n",
|
|
"\n",
|
|
"with h5py.File(glob.glob(f)[0]) as f:\n",
|
|
" print(\"available datasets\", list(f))\n",
|
|
" assert np.allclose(f[\"datasets/flopping_f_brightness\"], d)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Overwriting repository/notebook_test.py\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%writefile repository/notebook_test.py\n",
|
|
"\n",
|
|
"# we can also write experiments in the notebook and submit them\n",
|
|
"# we don't have submit-by-content yet (and there would be questions\n",
|
|
"# about other modules that would need to be imported) so we just export\n",
|
|
"# this cell and submit it by filename\n",
|
|
"\n",
|
|
"from artiq.experiment import *\n",
|
|
"\n",
|
|
"class Hello(EnvExperiment):\n",
|
|
" def build(self):\n",
|
|
" pass\n",
|
|
" \n",
|
|
" def run(self):\n",
|
|
" print(\"Hello world!\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"4724\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"expid = dict(\n",
|
|
" file=\"repository/notebook_test.py\",\n",
|
|
" class_name=\"Hello\",\n",
|
|
" log_level=logging.WARNING,\n",
|
|
" arguments=dict(),\n",
|
|
")\n",
|
|
"rid = schedule.submit(pipeline_name=\"misc\", expid=expid,\n",
|
|
" priority=1, due_date=None, flush=False)\n",
|
|
"print(rid)\n",
|
|
"# on the master you should see the message."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.5.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|