{
    "info": {
        "author": "TE-MPE",
        "author_email": "lhc-signal-monitoring@cern.ch",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# LHC Signal Monitoring API Package\n<img src=\"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/logo.png\" width=25%>\n\nThis is a package with an API for signal query and processing for the Signal Monitoring project.\n\n## Signal Monitoring Project Architecture\n\nThe Signal Monitoring project architecture consists of four elements:\n\n1. API for logging db query and signal processing (<a href=\"https://gitlab.cern.ch/lhcdata/lhc-sm-api\">https://gitlab.cern.ch/lhcdata/lhc-sm-api</a>)\n2. Signal Monitoring notebooks (<a href=\"https://gitlab.cern.ch/lhcdata/lhc-sm-apps\">https://gitlab.cern.ch/lhcdata/lhc-sm-apps</a>)\n3. HWC and Operation notebooks (<a href=\"https://gitlab.cern.ch/lhcdata/lhc-sm-hwc\">https://gitlab.cern.ch/lhcdata/lhc-sm-hwc</a>)\n4. Scheduler for execution of HWC notebooks and monitoring applications (<a href=\"https://gitlab.cern.ch/lhcdata/lhc-sm-scheduler\">https://gitlab.cern.ch/lhcdata/lhc-sm-scheduler</a>)\n<img src=\"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/lhc-sm-architecture.png\" width=50%>\n\n## Signal Monitoring API Architecture\n\n\nThe API is a collection of six modules for:  \n- database access (DBSignal); \n- signal, system, circuit naming (Metadata); \n- signal and event references (Reference); \n- embedded domain specific language for signal/feature/event query and processing, signal assertion, and feature engineering;\n- signal query, analysis, and plot (Analysis); \n- graphical user interfaces for browsing of historicalsignal features (GUI).\n\n<img src=\"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/lhc-sm-api-architecture.png\" width=50%>\n\n## Installation\nThere are two ways of using the API in your code:\n\n1. Loading pre-installed packages from an EOS project folder (in SWAN environment)\n2. Manual installation (in any environment)\n\nThe first option guarantees the use of the most recent code version without manual installation. The second one is more time consuming, however, works in environments with no access to the EOS folder (e.g., Apache Airflow scheduler). In addition, the second method allows to install a selected version (`pip install package_name=version`).\n\n### Preinstalled Packages\nTo use the set of pre-installed packages please follow these three steps:\n\n1. Contact the Signal Monitoring team (mailto:lhc-signal-monitoring@cern.ch) in order to get read access to the EOS folder with pre-installed packages.\n2. (optional) Uninstall existing packages.  \nInitially, the packages were installed manually as discussed in Section Manual Installation. Depending on the settings, a python script (or notebook) might use manually installed packages instead of the pre-installed ones.\nThus, to avoid issues related to a double reference to a package, please uninstall (with `pip uninstall package_name`) all packages required by the API (tzlocal, tqdm, influxdb, plotly, lhcsmapi). \nThis operation has to be done only once provided that the packages were installed (to check if a package was installed use `pip list | grep package_name` in SWAN Command Line Interface).\n3. While logging to SWAN service, please add the environment script as `/eos/project/l/lhcsm/public/packages.sh`\n<img src=\"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/swan_environment_script.png\" width=50%>\n\n### Manual Installation\nIn order to use the API, it has to be installed with a python package installer as\n\n```python\npip install --user lhcsmapi\n```\nCheck the latest version at <a href=\"https://pypi.org/project/lhcsmapi/\">https://pypi.org/project/lhcsmapi/</a>\n\nThe API relies on several external python packages which have to be installed in a similar manner. The list of packages is stored in the <u><i>requirements.txt</i></u> file.\n\nIf you use SWAN, the service provides a set of pre-installed python packages through CVMFS. The LHC-SM notebooks require installation of several additional packages on top of CVMFS. In order to install a package, please open a SWAN Terminal by clicking [>_] icon in the top right corner.\n\n![SWAN CLI Button](https://gitlab.cern.ch/LHCData/lhc-sm-hwc/-/raw/master/figures/swan-cli-button.png)\n\nFive additional python packages have to be installed:\n- tzlocal - for time zone convertion\n- tqdm - for progress bar to track queries\n- influxdb - for communication with an Influxdb\n- plotly - for interactive plotting of circuit schematics \n- lhcsmapi - for LHC-SM API\nIn order to install a package please execute the following command\n```\n$ pip install --user package_name\n```\n\nThe expected output, after installing all packages, is presented in five figures below.\n\n- SWAN Terminal output after successful installation of tzlocal package.\n![SWAN pip install tzlocal](https://gitlab.cern.ch/LHCData/lhc-sm-hwc/-/raw/master/figures/swan-pip-install-tzlocal.png)\n\n- SWAN Terminal output after successful installation of tqdm package.\n![SWAN pip install tqdm](https://gitlab.cern.ch/LHCData/lhc-sm-hwc/-/raw/master/figures/swan-pip-install-tqdm.png)\n\n- SWAN Terminal output after successful installation of influxdb package.\n![SWAN pip install influxdb](https://gitlab.cern.ch/LHCData/lhc-sm-hwc/-/raw/master/figures/swan-pip-install-influxdb.png)\n\n- SWAN Terminal output after successful installation of plotly package.\n![SWAN pip install plotly](https://gitlab.cern.ch/LHCData/lhc-sm-hwc/-/raw/master/figures/swan-pip-install-plotly.png)\n\n- SWAN Terminal output after successful installation of lhcsmapi package.\n![SWAN pip install lhcsmapi](https://gitlab.cern.ch/LHCData/lhc-sm-hwc/-/raw/master/figures/swan-pip-install-lhcsmapi.png)\n\n### Updating lhcsmapi Package\n\nPlease note that the first four packages (tzlocal, tqdm, influxdb, plotly) have to be installed only once while the last one is still in the development phase and subject to frequent updates. Please send us an e-mail request (mailto:lhc-signal-monitoring@cern.ch) if you want to subscribe for updates. In order to update the lhcsmapi package, please execute the following command.\n```\n$ pip install --user --upgrade lhcsmapi\n```\n\n### Known Issues\nAt times, in order to update the lhcsmapi package one has to execute the command\n```\npip install --user --upgrade lhcsmapi\n```\ntwice while using the SWAN terminal (cf. an error message in the figure below).\n<img src=\"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/double_reinstallation_error.png\">\n\n\nIn case this command returns an error, please try to execute it again. Should that operation also fail, please uninstall the package by executing\n```\n$ pip uninstall lhcsmapi\n```\n\nand performing a fresh installation the package\n```\n$ pip install --user lhcsmapi\n```\n\nShould you experience any further issues with installing a package, please contact <a href=\"https://swan.web.cern.ch\">SWAN support</a> or use the preinstalled package with the environment script.\n\n### NXCALS Access with SWAN\nThe API allows to perform a query of signals from PM and NXCALS. The NXCALS database requires an assignment of dedicated access rights for a user. \nIf you want to query NXCALS with the API, please follow a procedure below on how to request the NXCALS access.\n\n1. Go to http://nxcals-docs.web.cern.ch/current/user-guide/data-access/nxcals-access-request/ for most updated procedure\n2. Send an e-mail to mailto:acc-logging-support@cern.ch with the following pieces of information:\n - your NICE username\n - system: WinCCOA, CMW\n - NXCALS environment: PRO\n \nOptionally one can mention that the NXCALS database will be accessed through SWAN.\nOnce the access is granted, you can use NXCALS with SWAN.\n\n# 1. Time\n**Time** class encapsulates provides a set of methods for conversion between various time formats. In addition it provides a generator for days between given periods. Main methods are:\n- Time.to_unix_timestamp(input, unit='ns')\n- Time.to_pandas_timestamp(input, unit='ns')\n- Time.to_string(input, unit='ns')\n- Time.to_datetime(input, unit='ns')\n\nThe unit argument is optional. Its default value is 'ns', i.e., nanosecond.\n\n## 1.1. Time.to_unix_timestamp(input, unit='ns')\nThis method is useful to obtain a timestamp to query PM database\n\n\n```python\nfrom lhcsmapi.Time import Time\ndate_time_str = \"2018-05-01 00:00:00\"\nTime.to_unix_timestamp(date_time_str, unit=\"ns\")\n```\n\n\n\n\n    1525125600000000000\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\nfrom datetime import datetime\ndate_time_dt = datetime(2018, 5, 1, 0, 0, 0, 0)\nTime.to_unix_timestamp(date_time_dt, unit=\"ns\")\n```\n\n\n\n\n    1525125600000000000\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\ntimestamp = 1525125600000000000\nTime.to_unix_timestamp(timestamp, unit=\"ns\")\n```\n\n\n\n\n    1525125600000000000\n\n\n\nAny method also works without the unit. The default unit is ns.\n\n\n```python\nfrom lhcsmapi.Time import Time\ntimestamp = 1525125600000000000\nTime.to_unix_timestamp(timestamp)\n```\n\n\n\n\n    1525125600000000000\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\nimport pandas as pd\ntimestamp_pd = pd.Timestamp(2018, 5, 1, 0)\nTime.to_unix_timestamp(timestamp_pd, unit=\"ns\")\n```\n\n\n\n\n    1525125600000000000\n\n\n\n## 1.2. Time.to_pandas_timestamp(input, unit='ns')\n\n\n```python\nfrom lhcsmapi.Time import Time\ndate_time_str = \"2019-07-10 10:23:27.013\"\nTime.to_pandas_timestamp(date_time_str, unit=\"ns\")\n```\n\n\n\n\n    Timestamp('2019-07-10 10:23:27.013000+0200', tz='Europe/Zurich')\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\nfrom datetime import datetime\ndate_time_dt = datetime(2019, 7, 10, 10, 23, 27, 13)\nTime.to_pandas_timestamp(date_time_dt, unit=\"ns\")\n```\n\n\n\n\n    Timestamp('2019-07-10 10:23:27.000013+0200', tz='Europe/Zurich')\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\ntimestamp = 1562747007013000000\nTime.to_pandas_timestamp(timestamp, unit=\"ns\")\n```\n\n\n\n\n    Timestamp('2019-07-10 10:23:27.013000+0200', tz='Europe/Zurich')\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\nimport pandas as pd\ntimestamp_pd = pd.Timestamp(2019, 7, 10, 10, 23, 27, 13000)\nTime.to_pandas_timestamp(timestamp_pd, unit=\"ns\")\n```\n\n\n\n\n    Timestamp('2019-07-10 10:23:27.013000+0200', tz='Europe/Zurich')\n\n\n\n## 1.3. Time.to_string(input, unit='ns')\n\n\n```python\nfrom lhcsmapi.Time import Time\ndate_time_str = \"2017-07-02 01:23:27\"\nTime.to_string(date_time_str, unit=\"ns\")\n```\n\n\n\n\n    '2017-07-02 01:23:27+02:00'\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\nfrom datetime import datetime\ndate_time_dt = datetime(2017, 7, 2, 1, 23, 27, 0)\nTime.to_string(date_time_dt, unit=\"ns\")\n```\n\n\n\n\n    '2017-07-02 01:23:27+02:00'\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\ntimestamp = 1498951407000000000\nTime.to_string(timestamp, unit=\"ns\")\n```\n\n\n\n\n    '2017-07-02 01:23:27+02:00'\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\nimport pandas as pd\ntimestamp_pd = pd.Timestamp(2018, 5, 1, 1, 23, 27)\nTime.to_string(timestamp_pd, unit=\"ns\")\n```\n\n\n\n\n    '2018-05-01 01:23:27+02:00'\n\n\n\nThere is also a version of this method returning a string without the time zone\n\n\n```python\nfrom lhcsmapi.Time import Time\nimport pandas as pd\ntimestamp_pd = pd.Timestamp(2018, 5, 1, 1, 23, 27)\nTime.to_string_short(timestamp_pd, unit=\"ns\")\n```\n\n\n\n\n    '2018-05-01 01:23:27'\n\n\n\n## 1.4. Time.to_datetime(input, unit='ns')\n\n\n```python\nfrom lhcsmapi.Time import Time\ndate_time_str = \"2019-01-02 23:59:11\"\nTime.to_datetime(date_time_str, unit=\"ns\")\n```\n\n\n\n\n    datetime.datetime(2019, 1, 2, 23, 59, 11, tzinfo=<DstTzInfo 'Europe/Zurich' CET+1:00:00 STD>)\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\nfrom datetime import datetime\ndate_time_dt = datetime(2019, 1, 2, 23, 59, 11, 0)\nTime.to_datetime(date_time_dt, unit=\"ns\")\n```\n\n\n\n\n    datetime.datetime(2019, 1, 2, 23, 59, 11, tzinfo=<DstTzInfo 'Europe/Zurich' CET+1:00:00 STD>)\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\ntimestamp = 1546469951000000000\nTime.to_datetime(timestamp, unit=\"ns\")\n```\n\n\n\n\n    datetime.datetime(2019, 1, 2, 23, 59, 11, tzinfo=<DstTzInfo 'Europe/Zurich' CET+1:00:00 STD>)\n\n\n\n\n```python\nfrom lhcsmapi.Time import Time\nimport pandas as pd\ntimestamp_pd = pd.Timestamp(2019, 1, 2, 23, 59, 11, 0)\nTime.to_datetime(timestamp_pd, unit=\"ns\")\n```\n\n\n\n\n    datetime.datetime(2019, 1, 2, 23, 59, 11, tzinfo=<DstTzInfo 'Europe/Zurich' CET+1:00:00 STD>)\n\n\n\n## 1.5. Time.to_unix_timestamp_in_sec(input, tz)\n\n\n```python\nfrom lhcsmapi.Time import Time\ntimestamp = 1426220469491000000\nTime.to_unix_timestamp_in_sec(timestamp)\n```\n\n\n\n\n    1426220469.4910002\n\n\n\n## 1.6. Time.get_query_period_in_unix_time(t_start, t_end, duration, tz)\n\nThere are three ways of executing this method:\n- start time and end time\n\n\n```python\nfrom lhcsmapi.Time import Time\ntime_date_start = 1426220459491000000\ntime_date_end = 1426220479491000000\nTime.get_query_period_in_unix_time(time_date_start, time_date_end, None, tz='Europe/Zurich')\n```\n\n\n\n\n    (1426220459491000000, 1426220479491000000)\n\n\n\n- start time, duration with a single element list to indicate end time after the start time\n\n\n```python\nfrom lhcsmapi.Time import Time\ntime_date_start = 1426220459491000000\nduration = [(20, 's')]\nTime.get_query_period_in_unix_time(time_date_start, None, duration, tz='Europe/Zurich')\n```\n\n\n\n\n    (1426220459491000000, 1426220479491000000)\n\n\n\n- start time, duration with a two element list to indicate end time befor (1st element) and after (2nd element) the start time\n\n\n```python\nfrom lhcsmapi.Time import Time\ntime_date_start = 1426220469491000000\nduration =  [(10, 's'), (10, 's')]\nTime.get_query_period_in_unix_time(time_date_start, None, duration, tz='Europe/Zurich')\n```\n\n\n\n\n    (1426220459491000000, 1426220479491000000)\n\n\n\n## 1.7. Time.daterange(time_date_start, time_date_end)\nUseful to iterate over days while e.g. sequentially querying PM database for events (there is a limit of 24h query duration in this case).\n\n\n```python\nfrom datetime import date\ntime_date_start = date(2014, 1, 1)\ntime_date_end = date(2014, 1, 3)\nfor single_date in Time.daterange(time_date_start, time_date_end):\n    print(single_date)\n```\n\n    2014-01-01\n    2014-01-02\n\n\nIn case the start date is later than the end date, the generator does not return output\n\n\n```python\nfrom datetime import date\ntime_date_start = date(2014, 1, 5)\ntime_date_end = date(2014, 1, 3)\nfor single_date in Time.daterange(time_date_start, time_date_end):\n    print(single_date)\n```\n\n# 2. Timer\n**Timer** class provides a base functionality for measuring code execution time.\n\n\n```python\nfrom lhcsmapi.Timer import Timer\nt = Timer()\n\nwith Timer():\n    for i in range(1000000):\n        i = i+1\n```\n\n    Elapsed: 0.178 s.\n\n\nequivalent to\n\n\n```python\nimport time\nt = time.time()\nfor i in range(1000000):\n        i = i+1\ntime.time() - t\n```\n\n\n\n\n    0.1734294891357422\n\n\n\n# 3. Metadata\nThe Metadata (lhcsmapi.metadata) module contains methods to retrieve various signal and circuit names.  \nIn order to avoid storing full names and enable updating signal names based on time. A signal hierarchy is encoded with a dictionary and can be accessed through links in the table below\n\n|Circuit type|Hyperlink|\n|------------|---------|\n|RB          |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/RB_METADATA.json|\n|RQ          |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/RQ_METADATA.json|\n|IT          |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/IT_METADATA.json|\n|IPQ2        |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/IPQ2_METADATA.json|\n|IPQ4        |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/IPQ4_METADATA.json|\n|IPQ8        |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/IPQ8_METADATA.json|\n|IPD2        |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/IPD2_METADATA.json|\n|IPD2_B1B2   |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/IPD2_B1B2_METADATA.json|\n|60A         |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/60A_METADATA.json|\n|80-120A     |https://gitlab.cern.ch/LHCData/lhc-sm-api/blob/master/lhcsmapi/metadata/80-120A_METADATA.json|\n\n### 3.1. Signal Metadata \n**SignalMetadata** class stores information about signal and circuit names as well as corresponding metadata.\n\n- get beam mode description\n\n\n```python\nfrom lhcsmapi.metadata.SignalMetadata import SignalMetadata\n\nSignalMetadata.get_beam_mode_details()\n```\n\n\n\n\n<div>\n\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Mode</th>\n      <th>Name</th>\n      <th>Description</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>NOMODE</td>\n      <td>No mode, data is not available, not set</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>SETUP</td>\n      <td>Setup</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>INJPILOT</td>\n      <td>Pilot injection</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>INJINTR</td>\n      <td>Intermediate injection</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>INJNOMN</td>\n      <td>Nominal injection</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>PRERAMP</td>\n      <td>Before ramp</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>RAMP</td>\n      <td>Ramp</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>FLATTOP</td>\n      <td>Flat top</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>SQUEEZE</td>\n      <td>Squeeze</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>ADJUST</td>\n      <td>Adjust beam on flat top</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>STABLE</td>\n      <td>Stable beam for physics</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>UNSTABLE</td>\n      <td>Unstable beam</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>13</td>\n      <td>BEAMDUMP</td>\n      <td>Beam dump</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14</td>\n      <td>RAMPDOWN</td>\n      <td>Ramp down</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>RECOVERY</td>\n      <td>Recovering</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16</td>\n      <td>INJDUMP</td>\n      <td>Inject and dump</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17</td>\n      <td>CIRCDUMP</td>\n      <td>Circulate and dump</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18</td>\n      <td>ABORT</td>\n      <td>Recovery after a beam permit flag drop</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>19</td>\n      <td>CYCLING</td>\n      <td>Pre-cycle before injection, no beam</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>20</td>\n      <td>WBDUMP</td>\n      <td>Warning beam dump</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>21</td>\n      <td>NOBEAM</td>\n      <td>No beam or preparation for beam</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- get circuit types\n\n\n```python\nfrom lhcsmapi.metadata.SignalMetadata import SignalMetadata\n\nSignalMetadata.get_circuit_types()\n```\n\n\n\n\n    ['RB',\n     'RQ',\n     'IT',\n     'IPD2_B1B2',\n     'IPD2',\n     'IPQ2',\n     'IPQ4',\n     'IPQ8',\n     '600A',\n     '60A',\n     '80-120A']\n\n\n\n- get circuit names\n\n\n```python\nfrom lhcsmapi.metadata.SignalMetadata import SignalMetadata\n\nSignalMetadata.get_circuit_names_or_types('RB')\n```\n\n\n\n\n    ['RB.A12',\n     'RB.A23',\n     'RB.A34',\n     'RB.A45',\n     'RB.A56',\n     'RB.A67',\n     'RB.A78',\n     'RB.A81']\n\n\n\n- get signal name for PM\n\n\n```python\nfrom lhcsmapi.metadata.SignalMetadata import SignalMetadata\n\nSignalMetadata.get_signal_name('RB', 'RB.A12', 'PC', 'PM', 'I_MEAS')\n```\n\n\n\n\n    'STATUS.I_MEAS'\n\n\n\n- get signal name for CALS\n\n\n```python\nfrom lhcsmapi.metadata.SignalMetadata import SignalMetadata\n\nSignalMetadata.get_signal_name('RB', 'RB.A12', 'PC', 'CALS', 'I_MEAS')\n```\n\n\n\n\n    'RPTE.UA23.RB.A12:I_MEAS'\n\n\n\n- get signal name for NXCALS\n\n\n```python\nfrom lhcsmapi.metadata.SignalMetadata import SignalMetadata\n\nSignalMetadata.get_signal_name('RB', 'RB.A12', 'PC', 'NXCALS', 'I_MEAS')\n```\n\n\n\n\n    'I_MEAS'\n\n\n\n- get metadata for PM\n\n\n```python\nfrom lhcsmapi.metadata.SignalMetadata import SignalMetadata\n\nSignalMetadata.get_circuit_signal_database_metadata('RB', 'RB.A12', 'PC', 'PM')\n```\n\n\n\n\n    {'system': 'FGC',\n     'source': 'RPTE.UA23.RB.A12',\n     'className': '51_self_pmd',\n     'I_REF': 'STATUS.I_REF',\n     'I_MEAS': 'STATUS.I_MEAS',\n     'V_REF': 'STATUS.V_REF',\n     'V_MEAS': 'STATUS.V_MEAS',\n     'I_EARTH': 'IEARTH.IEARTH',\n     'I_EARTH_PCNT': 'STATUS.I_EARTH_PCNT',\n     'I_A': 'IAB.I_A',\n     'I_B': 'IAB.I_B'}\n\n\n\n- get metadata for NXCALS\n\n\n```python\nfrom lhcsmapi.metadata.SignalMetadata import SignalMetadata\n\nSignalMetadata.get_circuit_signal_database_metadata('RB', 'RB.A12', 'PC', 'NXCALS')\n```\n\n\n\n\n    {'system': 'CMW',\n     'device': 'RPTE.UA23.RB.A12',\n     'property': 'SUB',\n     'I_MEAS': 'I_MEAS'}\n\n\n\n### 3.2. Mapping Metadata\nSome signal names obtained with SignalMetadata functions have a wildcard in order to save space and exploit signal naming convention.  \n**MappingMetadata** class stores information about circuit topology, e.g., order and names of magnets in a particular circuit.  \nThere is a collection of csv files containing circuit topology summarised in the table below.\n\n|System type|Hyperlink|\n|------------|---------|\n|beam mode   |https://gitlab.cern.ch/LHCData/lhc-sm-api/tree/master/lhcsmapi/metadata/beam_mode|\n|busbar      |https://gitlab.cern.ch/LHCData/lhc-sm-api/tree/master/lhcsmapi/metadata/busbar|\n|magnet      |https://gitlab.cern.ch/LHCData/lhc-sm-api/tree/master/lhcsmapi/metadata/magnet|\n|qps_crate   |https://gitlab.cern.ch/LHCData/lhc-sm-api/tree/master/lhcsmapi/metadata/qps_crate|\n\n- QPS crate name for a magnet \n\n\n```python\nfrom lhcsmapi.metadata.MappingMetadata import MappingMetadata\n\nMappingMetadata.get_crate_name_from_magnet_name('RB', 'MB.A16L2')\n```\n\n\n\n\n    'B16L2'\n\n\n\n- QPS crates for RB\n\n\n```python\nfrom lhcsmapi.metadata.MappingMetadata import MappingMetadata\n\nMappingMetadata.get_crates_for_circuit_names('RB', 'RB.A12')\n```\n\n\n\n\n    ['B8L2',\n     'B10L2',\n     'B12L2',\n     'B14L2',\n     'B16L2',\n     'B18L2',\n     'B20L2',\n     'B22L2',\n     'B24L2',\n     'B26L2',\n     'B28L2',\n     'B30L2',\n     'B32L2',\n     'B34L2',\n     'B33R1',\n     'B31R1',\n     'B29R1',\n     'B27R1',\n     'B25R1',\n     'B23R1',\n     'B21R1',\n     'B19R1',\n     'B17R1',\n     'B15R1',\n     'B13R1',\n     'B11R1',\n     'B9R1',\n     'B8R1',\n     'B10R1',\n     'B12R1',\n     'B14R1',\n     'B16R1',\n     'B18R1',\n     'B20R1',\n     'B22R1',\n     'B24R1',\n     'B26R1',\n     'B28R1',\n     'B30R1',\n     'B32R1',\n     'B34R1',\n     'B33L2',\n     'B31L2',\n     'B29L2',\n     'B27L2',\n     'B25L2',\n     'B23L2',\n     'B21L2',\n     'B19L2',\n     'B17L2',\n     'B15L2',\n     'B13L2',\n     'B11L2',\n     'B9L2']\n\n\n\n# 4. Reference\nThe Reference module stores information about reference signal profiles and features.\n\n|System type|Hyperlink|\n|-----------------|---------|\n|Energy Extraction|https://gitlab.cern.ch/LHCData/lhc-sm-api/tree/master/lhcsmapi/reference/ee|\n|Current Leads    |https://gitlab.cern.ch/LHCData/lhc-sm-api/tree/master/lhcsmapi/reference/leads|\n|Power Converter  |https://gitlab.cern.ch/LHCData/lhc-sm-api/tree/master/lhcsmapi/reference/pc|\n|Quench Heaters   |https://gitlab.cern.ch/LHCData/lhc-sm-api/tree/master/lhcsmapi/reference/qh|\n\nReferences useful for analysis, so that we can measure deviation of signals.  \nReferences change over time, so we need to keep track of the changes.\n\n## 4.1. Get Reference Quench Heater Discharge Timestamp\n\n\n```python\nfrom lhcsmapi.reference.Reference import Reference\n\nReference.get_quench_heater_reference_discharge('RQ', '16L2')\n```\n\n\n\n\n    1417079205409000000\n\n\n\n## 4.2. Get Reference Power Converter Timestamp\n\n\n```python\nfrom lhcsmapi.reference.Reference import Reference\n\nReference.get_power_converter_reference_fpa('RQ', 'RQD.A12', 'fgcPm')\n```\n\n\n\n\n    '2018-03-18 11:58:33.600'\n\n\n\n## 4.3. Get Reference Energy Extraction Timestamp\n\n\n```python\nfrom lhcsmapi.reference.Reference import Reference\n\nReference.get_power_converter_reference_fpa('RQ', 'RQD.A12', 'eePm')\n```\n\n\n\n\n    '2018-03-18 11:58:33.675'\n\n\n\n## 4.4. Get Reference Energy Extraction Parameters\n\n\n```python\nfrom lhcsmapi.reference.Reference import Reference\n\nReference.read_energy_extraction_reference_features('RB', '2018-03-18 11:58:33.675')\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>min</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>R_1</th>\n      <td>0.0675</td>\n      <td>0.0825</td>\n    </tr>\n    <tr>\n      <th>R_2</th>\n      <td>0.0675</td>\n      <td>0.0825</td>\n    </tr>\n    <tr>\n      <th>t_delay_ee_1</th>\n      <td>0.0500</td>\n      <td>0.1500</td>\n    </tr>\n    <tr>\n      <th>t_delay_ee_2</th>\n      <td>0.5500</td>\n      <td>0.6500</td>\n    </tr>\n    <tr>\n      <th>tau_u_dump_res_1</th>\n      <td>110.0000</td>\n      <td>130.0000</td>\n    </tr>\n    <tr>\n      <th>tau_u_dump_res_2</th>\n      <td>110.0000</td>\n      <td>130.0000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom lhcsmapi.reference.Reference import Reference\n\nReference.read_energy_extraction_reference_features('RQ', '2018-03-18 11:58:33.675')\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>min</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>t_delay_ee</th>\n      <td>0.085</td>\n      <td>0.115</td>\n    </tr>\n    <tr>\n      <th>tau_u_dump_res</th>\n      <td>25.000</td>\n      <td>35.000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n# 5. PmDbQuery\n**PmDbRequest** class encapsulates PM database queries in order to simplify the query creation as well as browsing of PM events for a given period.\n\n- Expected Success Response\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal import PmDbRequest\nsource = 'RPTE.UA47.RB.A45'\nsystem = 'FGC'\nclassName = '51_self_pmd'\nt_start = '2015-03-13 05:20:59.4910002'\nduration = [(100, 's'), (100, 's')]\nPmDbRequest.find_events(source=source, system=system, className=className, t_start=t_start, duration=duration)\n```\n\n\n\n\n    [('RPTE.UA47.RB.A45', 1426220469520000000)]\n\n\n\nSource can be also replaced with a wildcard '*' in order to retrieve all sources for a given system, className, and duration.\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal import PmDbRequest\nsource = \"*\"\nsystem = \"QPS\"\nclassName = \"DQAMCNMB_PMHSU\"\nts_start = 1426201200000000000\nevent_duration_in_sec = 24*60*60 # 1 day is the maximum duration for a single query\n\nPmDbRequest.find_events(source, system, className, t_start=ts_start, duration=[(event_duration_in_sec, 's')])\n```\n\n\n\n\n    [('B20L5', 1426220469491000000),\n     ('C20L5', 1426220517100000000),\n     ('A20L5', 1426220518112000000),\n     ('A21L5', 1426220625990000000),\n     ('B21L5', 1426220866112000000),\n     ('C23L4', 1426236802332000000),\n     ('B23L4', 1426236839404000000),\n     ('A23L4', 1426236839832000000),\n     ('C22L4', 1426236949841000000),\n     ('C15R4', 1426251285711000000),\n     ('B15R4', 1426251337747000000),\n     ('A15R4', 1426251388741000000),\n     ('B34L8', 1426258716281000000),\n     ('C34L8', 1426258747672000000),\n     ('A34L8', 1426258747370000000),\n     ('C33L8', 1426258835955000000),\n     ('C34R7', 1426258853947000000),\n     ('A34R7', 1426258854113000000),\n     ('A20R3', 1426267931956000000),\n     ('B20R3', 1426267983579000000),\n     ('C20R3', 1426268004144000000),\n     ('B18L5', 1426277626360000000),\n     ('A18L5', 1426277679838000000),\n     ('C18L5', 1426277680496000000),\n     ('A19L5', 1426277903449000000)]\n\n\n\n- Expected Response with no data or wrong input\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal import PmDbRequest\nsource = \"*\"\nsystem = \"QPS\"\nclassName = \"DQAMCNMB_PMHSU\"\nts_start = 1426201200000000000\nevent_duration_in_sec = 1\n\nPmDbRequest.find_events(source, system, className, t_start=ts_start, duration=[(event_duration_in_sec, 's')])\n```\n\n\n\n\n    []\n\n\n\n# 6. DbSignal\n\nWe recommend the use of general purpose browsers for ad-hoc queries and exploration for signal hierarchy:\n- The new PM web browser is available at the following link: http://post-mortem-paas.web.cern.ch/\n- The new TIMBER NXCALS web browser is available at: https://timber.cern.ch/\n- In order to find the mapping between CALS variables and NXCALS variable and metadata (along with the data migration status), please consult: https://ccde.cern.ch/dataBrowser/search?acwReportName=CALS-to-NXCALS%20Data%20Migration&domainName=CALS\n\n\nIn order to programatically query PM, CALS, or NXCALS database, a dedicated API along with parameters is required. An overview is summarised in the table below.\n\n| &nbsp;          | PM                        | PM                                | CALS         | CALS*            | NXCALS                             | NXCALS                                       |\n|-----------------|---------------------------|-----------------------------------|--------------|------------------|------------------------------------|----------------------------------------------|\n| query type      | event                     | signal                            | signal       | feature          | signal                             | feature                                      |\n| input           | system, source, className | system, source, className, signal | signal       | signal, features | system, (device, property), signal | system, (device, property), signal, features |\n| time definition | time range                | timestamp                         | time range   | time range       | time range                         | time range                                   |\n| time unit       | ns                        | ns                                | s            | s                | ns                                 | ns                                           |\n| return type     | json                      | json                              | dict of list | dict of list     | spark dataframe                    | spark dataframe                              |\n| execution time  | fast                      | fast                              | can be slow  | fast             | slow                               | fast                                         |\n| execution type  | serial                    | serial                            | serial       | ?                | serial                             | parallel                                     |\n| use             | simple                    | simple                            | simple       | simple           | simple                             | requires good knowledge of spark             |\n\n\n\nThe DbSignal provides a unified access to PM, CALS, and NXCALS databases in order to return a common data format, a pandas DataFrame. This enables a more advanced signal processing capabilities. The DbSignal module uses native API for each database:\n- PM REST API: http://pm-api-pro/\n- pytimber CALS API: https://github.com/rdemaria/pytimber\n- NXCALS python API: http://nxcals-docs.web.cern.ch/0.4.11/\n\n\n- PM signal query\n\n\n```python\nimport requests\nimport json\n\nresponse = requests.get('http://pm-api-pro/v2/pmdata/signal?system=FGC&className=51_self_pmd&source=RPTE.UA47.RB.A45&timestampInNanos=1426220469520000000&signal=STATUS.I_MEAS')\njson_response = json.loads(response.text)\n\nname = json_response['content'][0]['namesAndValues'][0]['name']\ntime = json_response['content'][0]['namesAndValues'][1]['value']\nvalue = json_response['content'][0]['namesAndValues'][0]['value']\n\npd.DataFrame(value, time, [name]).plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f458c8fb630>\n\n\n\n- CALS signal query\n\n\n```python\nimport pandas as pd\nimport pytimber\nldb = pytimber.LoggingDB()\n\ni_meas = ldb.get(\"RPTE.UA47.RB.A45:I_MEAS\", '2015-03-13 05:20:49.4910002', '2015-03-13 05:22:49.4910002')\npd.DataFrame(i_meas.get(\"RPTE.UA47.RB.A45:I_MEAS\")[1], i_meas.get(\"RPTE.UA47.RB.A45:I_MEAS\")[0], [\"RPTE.UA47.RB.A45:I_MEAS\"]).plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4568867fd0>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_84_1.png)\n\n\n- NXCALS signal query\n\n\n```python\nfrom cern.nxcals.pyquery.builders import *\nimport pandas as pd\n\ni_meas_df = DevicePropertyQuery.builder(spark).system('CMW') \\\n    .startTime(pd.Timestamp('2015-03-13T04:20:59.491000000').to_datetime64())\\\n    .endTime(pd.Timestamp('2015-03-13T04:22:49.491000000')) \\\n    .entity().device('RPTE.UA47.RB.A45').property('SUB') \\\n    .buildDataset().select('acqStamp', 'I_MEAS').dropna().sort(\"acqStamp\").toPandas()\n\ni_meas_df.set_index(i_meas_df['acqStamp'], inplace=True)\ni_meas_df.drop(columns='acqStamp', inplace=True)\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45681447f0>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_86_1.png)\n\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\n\nbmode_df = Signal().read('nxcals', nxcals_system='CMW',\n              signal='HX:BMODE', t_start='2015-03-13 05:20:59.4910002', t_end='2015-04-13 05:20:59.4910002', spark=spark)\nbmode_df.head()\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>HX:BMODE</th>\n    </tr>\n    <tr>\n      <th>nxcals_timestamp</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1426513864882000000</th>\n      <td>SETUP</td>\n    </tr>\n    <tr>\n      <th>1426515551191000000</th>\n      <td>NOBEAM</td>\n    </tr>\n    <tr>\n      <th>1426597426610000000</th>\n      <td>SETUP</td>\n    </tr>\n    <tr>\n      <th>1426597576404000000</th>\n      <td>STABLE</td>\n    </tr>\n    <tr>\n      <th>1426598591040000000</th>\n      <td>UNSTABLE</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\nSignal class encapsulates behavior for working with signal databases. It provides read and write methods for accessing Post Mortem, CALS and InfluxDB APIs.\n<img src = \"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/signal.png\" width=100%>\n\nRead method supports only scalar arguments and returns a pandas Series.  \nIn case of errors, the read method returns a warning message with the raw API use to query in order to quickly reproduce the error. This way, one can exclude the error introduced by the lhcsmapi.\n\n## 6.1. PM Database\n\n- PM Success Response\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nsystem = 'FGC'\nsource = 'RPTE.UA47.RB.A45'\nclassName = '51_self_pmd'\nsignal = 'STATUS.I_MEAS'\neventTime = 1426220469520000000\ni_meas_df = Signal().read('pm', system=system, source=source, className=className, \n                          signal=signal, eventTime=eventTime)\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f456813ad30>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_91_1.png)\n\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nsystem = \"QPS\"\nclassName = \"DQAMCNMB_PMHSU\"\nmagnet = \"B20L5\"\nsignal = \"I_HDS_4\"\ntimestamp = 1426220469491000000\ni_hds_4_df = Signal().read('pm', system=system, className=className, source=magnet,\n                           signal=signal, eventTime=timestamp)\ni_hds_4_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f456834c7f0>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_92_1.png)\n\n\n- PM Fail Response\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nsystem = \"QPS\"\nclassName = \"DQAMCNMB_PMHSU\"\nmagnet = \"B20L5\"\nsignal = \"I_HDS_4\"\ntimestamp = 142622046949100000 # last zero removed\ni_hds_4_df = Signal().read('pm', system=system, className=className, source=magnet,\n                              signal=signal, eventTime=timestamp)\n```\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/dbsignal/post_mortem/PmDbRequest.py:148: UserWarning: Querying Post Mortem failed using the following query: http://pm-api-pro/v2/pmdata/signal?system=QPS&className=DQAMCNMB_PMHSU&source=B20L5&timestampInNanos=142622046949100000&signal=I_HDS_4\n      warnings.warn(warning)\n\n\n## 6.2. CALS Database\n\n- CALS Success Response\n\n(t_start, t_end)\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nimport pytimber\nldb = pytimber.LoggingDB()\n\nsignal = \"RPTE.UA47.RB.A45:I_MEAS\"\nts_start = 1426220459491000000\nts_end = 1426220479491000000\ni_meas_df = Signal().read('cals', signal=signal, t_start=ts_start, t_end=ts_end, ldb=ldb)\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4552b5cda0>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_97_1.png)\n\n\n(t_start, t_start+duration[0])\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nimport pytimber\nldb = pytimber.LoggingDB()\n\nsignal = \"RPTE.UA47.RB.A45:I_MEAS\"\nts_start = 1426220459491000000\nduration = [(20, 's')]\ni_meas_df = Signal().read('cals', signal=signal, t_start=ts_start, duration=duration, ldb=ldb)\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4487655780>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_99_1.png)\n\n\n(t_start-duration[0], t_start+duration[1])\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nimport pytimber\nldb = pytimber.LoggingDB()\n\nsignal = \"RPTE.UA47.RB.A45:I_MEAS\"\nts_start = 1426220469491000000\nduration = [(10, 's'), (10, 's')]\ni_meas_df = Signal().read('cals', signal=signal, t_start=ts_start, duration=duration, ldb=ldb)\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f44876351d0>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_101_1.png)\n\n\n- CALS Fail Response\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nimport pytimber\nldb = pytimber.LoggingDB()\n\nsignal = \"RPTE.UA47.RB.A45:I_MEA\" # last S missing\nts_start = 1426220469491000000\nduration = [(10, 's'), (10, 's')]\ni_meas_df = Signal().read('cals', signal=signal, t_start=ts_start, duration=duration, ldb=ldb)\n```\n\n    WARNING:pytimber.pytimber:No variables found.\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/dbsignal/cals/CalsDbSignal.py:179: UserWarning: Querying CALS with the parameters signal: RPTE.UA47.RB.A45:I_MEA, start time: 1426220459.4910002 and end time: 1426220479.4910002 returned no data.\n                An empty DataFrame is returned.\n                ___________________________________________________________________________________\n                Reproduce the error using the raw pytimber API:\n    \n                import pytimber\n                ldb = pytimber.LoggingDB()\n                ldb.get(\"RPTE.UA47.RB.A45:I_MEA\", 1426220459.4910002, 1426220479.4910002)\n      warnings.warn(warning)\n\n\n## 6.3. NXCALS Database\n\n- NXCALS Success Response\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\n\nnxcals_system = 'CMW'\nnxcals_device = 'RPTE.UA47.RB.A45'\nnxcals_property = 'SUB'\nsignal = 'I_MEAS'\nt_start = 1426220469491000000\nduration = [(10, 's'), (100, 's')]\n\ni_meas_df = Signal().read('nxcals', nxcals_system=nxcals_system,  nxcals_device=nxcals_device, nxcals_property=nxcals_property, \n                          signal=signal, t_start=t_start, duration=duration, spark=spark)\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45681ac0f0>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_106_1.png)\n\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nnxcals_system = 'WINCCOA'\n\nsignal = 'DCBA.15R4.R:U_MAG'\nt_start = 1426220469491000000\nduration = [(10, 's'), (100, 's')]\n\nu_mag_df = Signal().read('nxcals', nxcals_system=nxcals_system,\n                          signal=signal, t_start=t_start, duration=duration, spark=spark)\nu_mag_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45680e17b8>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_107_1.png)\n\n\n- NXCALS Fail Response\n\n\n```python\nfrom lhcsmapi.pyedsl.dbsignal.Signal import Signal\nnxcals_system = 'CMW'\nnxcals_device = 'RPTE.UA47.RB.A4' # missing last 5\nnxcals_property = 'SUB'\nsignal = 'I_MEAS'\nt_start = 1426220469491000000\nduration = [(10, 's'), (100, 's')]\n\ni_meas_df = Signal().read('nxcals', nxcals_system=nxcals_system,  nxcals_device=nxcals_device, nxcals_property=nxcals_property, \n                          signal=signal, t_start=t_start, duration=duration, spark=spark)\ni_meas_df.plot()\n```\n\n\n    ---------------------------------------------------------------------------\n\n    Py4JJavaError                             Traceback (most recent call last)\n\n    /cvmfs/sft.cern.ch/lcg/views/LCG_95apython3_nxcals/x86_64-centos7-gcc7-opt/lib/python3.6/site-packages/pyspark/sql/utils.py in deco(*a, **kw)\n         62         try:\n    ---> 63             return f(*a, **kw)\n         64         except py4j.protocol.Py4JJavaError as e:\n\n\n    /cvmfs/sft.cern.ch/lcg/views/LCG_95apython3_nxcals/x86_64-centos7-gcc7-opt/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)\n        327                     \"An error occurred while calling {0}{1}{2}.\\n\".\n    --> 328                     format(target_id, \".\", name), value)\n        329             else:\n\n\n    Py4JJavaError: An error occurred while calling o208.select.\n    : org.apache.spark.sql.AnalysisException: cannot resolve '`acqStamp`' given input columns: [nxcals_entity_id, __record_version__, device, property, __record_timestamp__, class];;\n    'Project ['acqStamp, 'I_MEAS]\n    +- LocalRelation <empty>, [nxcals_entity_id#296L, device#297, property#298, class#299, __record_version__#300L, __record_timestamp__#301L]\n    \n    \tat org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)\n    \tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$3.applyOrElse(CheckAnalysis.scala:110)\n    \tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$3.applyOrElse(CheckAnalysis.scala:107)\n    \tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:278)\n    \tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:278)\n    \tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)\n    \tat org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:277)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsUp$1.apply(QueryPlan.scala:93)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsUp$1.apply(QueryPlan.scala:93)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$1.apply(QueryPlan.scala:105)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$1.apply(QueryPlan.scala:105)\n    \tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:104)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:116)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1$2.apply(QueryPlan.scala:121)\n    \tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)\n    \tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)\n    \tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n    \tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n    \tat scala.collection.TraversableLike$class.map(TraversableLike.scala:234)\n    \tat scala.collection.AbstractTraversable.map(Traversable.scala:104)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:121)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:126)\n    \tat org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:126)\n    \tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:93)\n    \tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:107)\n    \tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:85)\n    \tat org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)\n    \tat org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:85)\n    \tat org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:95)\n    \tat org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:108)\n    \tat org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:105)\n    \tat org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)\n    \tat org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:105)\n    \tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)\n    \tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)\n    \tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)\n    \tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:79)\n    \tat org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:3407)\n    \tat org.apache.spark.sql.Dataset.select(Dataset.scala:1335)\n    \tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n    \tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n    \tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n    \tat java.lang.reflect.Method.invoke(Method.java:498)\n    \tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n    \tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n    \tat py4j.Gateway.invoke(Gateway.java:282)\n    \tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n    \tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n    \tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n    \tat java.lang.Thread.run(Thread.java:745)\n\n\n    \n    During handling of the above exception, another exception occurred:\n\n\n    AnalysisException                         Traceback (most recent call last)\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/dbsignal/nxcals/NxcalsDbSignal.py in get_signal_df(signal, t_start, t_end, nxcals_system, nxcals_device, nxcals_property, spark)\n         69                     .entity().device(nxcals_device).property(nxcals_property) \\\n    ---> 70                     .buildDataset().select('acqStamp', signal).dropna().sort(\"acqStamp\").toPandas()\n         71                 signal_df.set_index(signal_df['acqStamp'], inplace=True)\n\n\n    /cvmfs/sft.cern.ch/lcg/views/LCG_95apython3_nxcals/x86_64-centos7-gcc7-opt/lib/python3.6/site-packages/pyspark/sql/dataframe.py in select(self, *cols)\n       1319         \"\"\"\n    -> 1320         jdf = self._jdf.select(self._jcols(*cols))\n       1321         return DataFrame(jdf, self.sql_ctx)\n\n\n    /cvmfs/sft.cern.ch/lcg/views/LCG_95apython3_nxcals/x86_64-centos7-gcc7-opt/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)\n       1256         return_value = get_return_value(\n    -> 1257             answer, self.gateway_client, self.target_id, self.name)\n       1258 \n\n\n    /cvmfs/sft.cern.ch/lcg/views/LCG_95apython3_nxcals/x86_64-centos7-gcc7-opt/lib/python3.6/site-packages/pyspark/sql/utils.py in deco(*a, **kw)\n         68             if s.startswith('org.apache.spark.sql.AnalysisException: '):\n    ---> 69                 raise AnalysisException(s.split(': ', 1)[1], stackTrace)\n         70             if s.startswith('org.apache.spark.sql.catalyst.analysis'):\n\n\n    AnalysisException: \"cannot resolve '`acqStamp`' given input columns: [nxcals_entity_id, __record_version__, device, property, __record_timestamp__, class];;\\n'Project ['acqStamp, 'I_MEAS]\\n+- LocalRelation <empty>, [nxcals_entity_id#296L, device#297, property#298, class#299, __record_version__#300L, __record_timestamp__#301L]\\n\"\n\n    \n    During handling of the above exception, another exception occurred:\n\n\n    Exception                                 Traceback (most recent call last)\n\n    <ipython-input-58-cfede295664b> in <module>\n          8 \n          9 i_meas_df = Signal().read('nxcals', nxcals_system=nxcals_system,  nxcals_device=nxcals_device, nxcals_property=nxcals_property, \n    ---> 10                           signal=signal, t_start=t_start, duration=duration, spark=spark)\n         11 i_meas_df.plot()\n\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/dbsignal/Signal.py in read(self, api, append, **kwargs)\n        165         api = api.upper()\n        166 \n    --> 167         df = Signal._database_access_handler(api, 'read', **kwargs)\n        168         if append:\n        169             if self.__df_state is not None and self.__df_state != api:\n\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/dbsignal/Signal.py in _database_access_handler(api, access_method_identifier, **kwargs)\n        204         try:\n        205             access_method = getattr(db_access, access_method_identifier, None)\n    --> 206             return access_method(**kwargs)\n        207         except AttributeError:\n        208             raise AttributeError('{} Signal has no {} method'.format(api, access_method_identifier))\n\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/dbsignal/nxcals/NxcalsDbSignal.py in read(self, signal, **kwargs)\n         59         \"\"\"\n         60         return NxcalsDbSignal.get_signal_df(self.signal, self.t_start, self.t_end,\n    ---> 61                                           self.nxcals_system, self.nxcals_device, self.nxcals_property, self.spark)\n         62 \n         63     @staticmethod\n\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/dbsignal/nxcals/NxcalsDbSignal.py in get_signal_df(signal, t_start, t_end, nxcals_system, nxcals_device, nxcals_property, spark)\n         80                             \"\"\".format(signal, t_start, t_end, nxcals_system, nxcals_device, nxcals_property)\n         81 \n    ---> 82                 raise Exception(exception)\n         83         else:\n         84             try:\n\n\n    Exception: Querying NXCALS with the parameters signal: I_MEAS, start time: 2015-03-13T04:20:59.491000000, end time: 2015-03-13T04:22:49.491000000, nxcals_system CMW, nxcals_device RPTE.UA47.RB.A4, nxcals_property SUB failed\n                                ___________________________________________________________________________________\n                                Reproduce the error using the raw NXCALS API:\n    \n                                from cern.nxcals.pyquery.builders import *\n                                DevicePropertyQuery.builder(spark).nxcals_system(''CMW'').startTime(''2015-03-13T04:20:59.491000000'').endTime(''2015-03-13T04:22:49.491000000'').entity().nxcals_device(''RPTE.UA47.RB.A4'').nxcals_property(''SUB'').buildDataset().select('acqStamp', ''signal'').dropna().sort(\"acqStamp\").toPandas()\n                                \n\n\n# 7. pyeDSL - Embedded Domain Specific Language in python\n\nThe Signal class serves it purpose to get a raw data for a single signal. However, it makes it difficult to generalize in order to acquire the same signal across several circuits. In addition, typical processing requires to use additional classes and introduce even more local variables\n\n```python\ncircuit_type = 'RB'\ncircuit_name = 'RB.A12'\nt_start = '2015-01-13 16:59:11+01:00'\nt_end = '2015-01-13 17:15:46+01:00'\ndb = 'NXCALS'\nsystem = 'PC'\n\nmetadata_pc = SignalMetadata.get_circuit_signal_database_metadata(circuit_type, circuit_name, system, db)\nI_MEAS = SignalMetadata.get_signal_name(circuit_type, circuit_name, system, db, 'I_MEAS')\n\ni_meas_nxcals_df = Signal().read(db, signal=I_MEAS, t_start=t_start, t_end=t_end, \n                                 nxcals_device=metadata_pc['device'], nxcals_property=metadata_pc['property'], nxcals_system=metadata_pc['system'], \n                                 spark=spark)\n\ni_meas_nxcals_df = SignalUtilities.synchronize_df(i_meas_nxcals_df)\ni_meas_nxcals_df = SignalUtilities.convert_indices_to_sec(i_meas_nxcals_df)\n\n```\n\nSeveral design flaws leading to inconsistency and code duplications:\n- <span style=\"color: red;\">use of multiple methods, multiple arguments (duplicated across methods)</span>\n- <span style=\"color: red;\">multiple local variables (naming consistency across analysis modules)</span>\n- <span style=\"color: red;\">order of methods and arguments (with duck typing) not fixed</span>\n\nThe API also does not answer in a generic way the following questions\n- What if we want to get current for each circuit?\n- What if we want to get several current signals?\n\nNatural languages have certain structure [1]\n\n|Language|Word order|Example|\n|--------|---------------------------|--------------|\n|English:| {Subject}.{Verb}.{Object}:| John ate cake|\n|Japanese:| {Subject}.{Order}.{Verb}:| John-ga keiki-o tabeta|\n| | | \t\t\tJohn cake ate |\n                \nOne can enforce syntactical order in code:\n - Domain Specific Language \u2013 new language, requires parser\n - Embedded Domain Specific Language \u2013 extends existing language\n \nFurthermore, an eDSL could be implemented following the Fluent interface approach [2]. The use of an eDSL for signal query and processing is not a new concept as there exists already an eDSL in Java used to automatically check signals during Hardware Commisionning campaigns of the LHC [3].\n\n[1] K. Gulordava, Word order variation and dependency length minimisation: a cross-linguistic computational approach, PhD thesis, UniGe,  \n[2] https://en.wikipedia.org/wiki/Fluent_interface  \n[3] M. Audrain, et al. - Using a Java Embedded Domain-Specific Language for LHC Test Analysis, ICALEPCS2013, San Francisco, CA, USA  \n\n\n## 7.1. QueryBuilder()\n\nWe propose a python embedded Domain Specific Language (pyeDSL) for building queries:\n- General purpose query\n<center>{DB}.{DURATION}.{QUERY_PARAMETERS}.{QUERY}</center>\n\ne.g.\n```python\ndf = QueryBuilder().{with_pm()/with_cals()/with_nxcals()}.with_duration().with_query_parameters()\\\n\t.signal_query().dfs[0]\ndf = QueryBuilder().{with_pm()/with_cals()/with_nxcals()}.with_timestamp().with_query_parameters()\\\n\t.signal_query().dfs[0]\n```\n\n- Circuit-oriented query to generalize query across and within circuit types\n<center>{DB}.{DURATION}.{CIRCUIT_TYPE}.{METADATA}.{QUERY}</center>\n\ne.g.  \n\n```python\ndf = QueryBuilder().{with_pm()/with_cals()/with_nxcals()}.with_duration().with_circuit_type().with_metadata()\\\n\t.signal_query().dfs[0]\ndf = QueryBuilder().{with_pm()/with_cals()/with_nxcals()}.with_timestamp().with_circuit_type().with_metadata()\\\n\t.signal_query().dfs[0]\n```\n\n+ <span style=\"color: green;\">each parameter defined once (validation of input at each stage)</span>\n+ <span style=\"color: green;\">single local variable</span>\n+ <span style=\"color: green;\">order of operation is fixed</span>\n+ <span style=\"color: green;\">support for vector inputs</span>\n+ <span style=\"color: green;\">time-dependent metadata</span>\n\n\nThe pyeDSL provides hints on the order of execution\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nQueryBuilder()\n```\n\n\n\n\n    Set database name using with_pm(), with_cals(ldb), with_nxcals(spark) method.\n\n\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nQueryBuilder().with_nxcals(spark)\n```\n\n\n\n\n    Database name properly set to NXCALS. Set time definition: for PM signal query, with_timestamp(),\n     for PM event query or CALS, NXCALS signal query, with_duration()\n\n\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nQueryBuilder().with_nxcals(spark).with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')])\n```\n\n\n\n\n    Query duration set to t_start=1426220359491000200, t_end=1426220559491000200. Set generic query parameter using with_query_parameters() method, or a circuit signal using with_circuit_type() method.\n\n\n\nAt the same time it prohibits unsupported operations throwing a meaningful exception\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nQueryBuilder().with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')])\n```\n\n\n    ---------------------------------------------------------------------------\n\n    AttributeError                            Traceback (most recent call last)\n\n    <ipython-input-62-17466de21b88> in <module>\n          1 from lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n          2 \n    ----> 3 QueryBuilder().with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')])\n    \n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/QueryBuilder.py in __getattr__(self, name)\n        132     def __getattr__(self, name):\n        133         if name not in ['query', 'with_pm', 'with_cals', 'with_nxcals']:\n    --> 134             raise AttributeError('{}() is not supported. {}'.format(name, self._msg))\n        135 \n        136     def __repr__(self):\n\n\n    AttributeError: with_duration() is not supported. Set database name using with_pm(), with_cals(ldb), with_nxcals(spark) method.\n\n\nSections 7.1.1 - 7.1.6. deal with: PM event and signal query, CALS signal query, NXCALS signal and feature query (as shown in Table 1). Each case is discussed with a general purpose query, where the user has to provide full data on signal name and its metadata as well as circuit-oriented queries which provide a generic way of querying LHC circuit variables. Both query types are polymorphic and complemented with a set of post-processing functions.\n\n*Since CALS is about to be discontinued at CERN, we do not support this type of query. For feature query with pytimber, please consult https://gitlab.cern.ch/scripting-tools/pytimber \n\n## 7.1.1. General-Purpose Query - Examples\nA sentence constructed this way maintains the differences of query types while providing a common structure\n\n- PM event query\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_query_parameters(system='FGC', className='51_self_pmd', source='RPTE.UA47.RB.A45') \\\n    .event_query().df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RPTE.UA47.RB.A45</td>\n      <td>1426220469520000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(24*60*60, 's')]) \\\n    .with_query_parameters(system='QPS', className='DQAMCNMB_PMHSU', source='*') \\\n    .event_query().df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B20L5</td>\n      <td>1426220469491000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C20L5</td>\n      <td>1426220517100000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A20L5</td>\n      <td>1426220518112000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A21L5</td>\n      <td>1426220625990000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B21L5</td>\n      <td>1426220866112000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>C23L4</td>\n      <td>1426236802332000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>B23L4</td>\n      <td>1426236839404000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A23L4</td>\n      <td>1426236839832000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>C22L4</td>\n      <td>1426236949841000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>C15R4</td>\n      <td>1426251285711000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>B15R4</td>\n      <td>1426251337747000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>A15R4</td>\n      <td>1426251388741000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>B34L8</td>\n      <td>1426258716281000000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>C34L8</td>\n      <td>1426258747672000000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>A34L8</td>\n      <td>1426258747370000000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>C33L8</td>\n      <td>1426258835955000000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>C34R7</td>\n      <td>1426258853947000000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>A34R7</td>\n      <td>1426258854113000000</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>A20R3</td>\n      <td>1426267931956000000</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>B20R3</td>\n      <td>1426267983579000000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>C20R3</td>\n      <td>1426268004144000000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>B18L5</td>\n      <td>1426277626360000000</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>A18L5</td>\n      <td>1426277679838000000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>C18L5</td>\n      <td>1426277680496000000</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>A19L5</td>\n      <td>1426277903449000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- PM signal query\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_pm() \\\n    .with_timestamp(1426220469520000000) \\\n    .with_query_parameters(system='FGC', source='RPTE.UA47.RB.A45', className='51_self_pmd', signal='STATUS.I_MEAS') \\\n    .signal_query().dfs[0]\n\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45680e6908>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_125_1.png)\n\n\n- CALS signal query\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\nimport pytimber\nldb = pytimber.LoggingDB()\n\ni_meas_df = QueryBuilder().with_cals(ldb) \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_query_parameters(signal='RPTE.UA47.RB.A45:I_MEAS') \\\n    .signal_query().dfs[0]\n\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4487590048>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_127_1.png)\n\n\n- NXCALS signal query - device, property\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_query_parameters(nxcals_system='CMW', nxcals_device='RPTE.UA47.RB.A45', nxcals_property='SUB', signal='I_MEAS') \\\n    .signal_query().dfs[0]\n\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f455364a278>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_129_1.png)\n\n\n- NXCALS signal query - variable\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_mag_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_query_parameters(nxcals_system='WINCCOA', signal='DCBA.15R4.R:U_MAG') \\\n    .signal_query().dfs[0]\n\nu_mag_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4553575e10>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_131_1.png)\n\n\n\n```python\nbmode_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', t_end='2015-04-13 05:20:59.4910002') \\\n    .with_query_parameters(nxcals_system='CMW', signal='HX:BMODE') \\\n    .signal_query().dfs[0]\n\nbmode_df.head()\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>HX:BMODE</th>\n    </tr>\n    <tr>\n      <th>nxcals_timestamp</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1426513864882000000</th>\n      <td>SETUP</td>\n    </tr>\n    <tr>\n      <th>1426515551191000000</th>\n      <td>NOBEAM</td>\n    </tr>\n    <tr>\n      <th>1426597426610000000</th>\n      <td>SETUP</td>\n    </tr>\n    <tr>\n      <th>1426597576404000000</th>\n      <td>STABLE</td>\n    </tr>\n    <tr>\n      <th>1426598591040000000</th>\n      <td>UNSTABLE</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- NXCALS feature query - device, property\n\nI_MEAS from beam injection, through beam acceleration, to stable beams\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=1526898157236000000, t_end=1526903352338000000) \\\n    .with_query_parameters(nxcals_system='CMW', nxcals_device='RPTE.UA23.RB.A12', nxcals_property='SUB', signal='I_MEAS') \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count']).df\n\ni_meas_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>device</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RPTE.UA23.RB.A12</td>\n      <td>10978.8</td>\n      <td>757.18</td>\n      <td>2498</td>\n      <td>5374.768659</td>\n      <td>3315.972856</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- NXCALS feature query - variable\n\nU_MAG from beam injection, through beam acceleration, to stable beams\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_mag_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=1526898157236000000, t_end=1526903352338000000) \\\n    .with_query_parameters(nxcals_system='WINCCOA', signal='DCBB.8L2.R:U_MAG') \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count']).df\n\nu_mag_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nxcals_variable_name</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>0.002113</td>\n      <td>-0.975332</td>\n      <td>51951</td>\n      <td>-0.196706</td>\n      <td>0.375195</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n## 7.1.2. General-Purpose Query - Polymorphism\n- multiple signal, mutliple sources, multiple systems, multiple className\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_pm() \\\n    .with_timestamp(1426220469520000000) \\\n    .with_query_parameters(system=['FGC', 'FGC'], source=['RPTE.UA47.RB.A45', 'RPTE.UA47.RB.A45'], \n                           className=['51_self_pmd', '51_self_pmd'], signal=['STATUS.I_MEAS', 'STATUS.I_REF']) \\\n    .signal_query().dfs\n\nax=i_meas_df[0].plot()\ni_meas_df[1].plot(ax=ax)\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45534e1e10>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_138_1.png)\n\n\n- multiple signal, single source, single system, single className\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_pm() \\\n    .with_timestamp(1426220469520000000) \\\n    .with_query_parameters(system='FGC', source='RPTE.UA47.RB.A45', \n                           className='51_self_pmd', signal=['STATUS.I_MEAS', 'STATUS.I_REF']) \\\n    .signal_query().dfs\n\nax=i_meas_df[0].plot()\ni_meas_df[1].plot(ax=ax)\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45533e2780>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_140_1.png)\n\n\n## 7.1.3. Circuit-Oriented Query - Examples\n- PM event query\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='PC') \\\n    .event_query().df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RPTE.UA47.RB.A45</td>\n      <td>1426220469520000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(24*60*60, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='QH', source='*') \\\n    .event_query().df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B20L5</td>\n      <td>1426220469491000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C20L5</td>\n      <td>1426220517100000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A20L5</td>\n      <td>1426220518112000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A21L5</td>\n      <td>1426220625990000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B21L5</td>\n      <td>1426220866112000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>C23L4</td>\n      <td>1426236802332000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>B23L4</td>\n      <td>1426236839404000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A23L4</td>\n      <td>1426236839832000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>C22L4</td>\n      <td>1426236949841000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>C15R4</td>\n      <td>1426251285711000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>B15R4</td>\n      <td>1426251337747000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>A15R4</td>\n      <td>1426251388741000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>B34L8</td>\n      <td>1426258716281000000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>C34L8</td>\n      <td>1426258747672000000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>A34L8</td>\n      <td>1426258747370000000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>C33L8</td>\n      <td>1426258835955000000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>C34R7</td>\n      <td>1426258853947000000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>A34R7</td>\n      <td>1426258854113000000</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>A20R3</td>\n      <td>1426267931956000000</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>B20R3</td>\n      <td>1426267983579000000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>C20R3</td>\n      <td>1426268004144000000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>B18L5</td>\n      <td>1426277626360000000</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>A18L5</td>\n      <td>1426277679838000000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>C18L5</td>\n      <td>1426277680496000000</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>A19L5</td>\n      <td>1426277903449000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- PM signal query\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_pm() \\\n    .with_timestamp(1426220469520000000) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='PC', signal='I_MEAS') \\\n    .signal_query().dfs[0]\n\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4553398208>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_145_1.png)\n\n\n- CALS signal query\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\nimport pytimber\nldb = pytimber.LoggingDB()\n\ni_meas_df = QueryBuilder().with_cals(ldb) \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='PC', signal='I_MEAS')\\\n    .signal_query().dfs[0]\n\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4487557908>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_147_1.png)\n\n\n- NXCALS signal query - device, property\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='PC', signal='I_MEAS') \\\n    .signal_query().dfs[0]\n\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f455336bd68>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_149_1.png)\n\n\n- NXCALS signal query - variable\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_mag_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='BUSBAR', signal='U_MAG', wildcard={'BUSBAR': 'DCBA.15R4.R'}) \\\n    .signal_query().dfs[0]\n\nu_mag_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4553273b70>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_151_1.png)\n\n\n- NXCALS feature query - device, property\n\nI_MEAS from beam injection, through beam acceleration, to stable beams\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=1526898157236000000, t_end=1526903352338000000) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='PC', signal='I_MEAS') \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count']).df\n\ni_meas_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>device</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RPTE.UA23.RB.A12</td>\n      <td>10978.8</td>\n      <td>757.18</td>\n      <td>2498</td>\n      <td>5374.768659</td>\n      <td>3315.972856</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- NXCALS feature query - variable\n\nU_MAG from beam injection, through beam acceleration, to stable beams\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_mag_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=1526898157236000000, t_end=1526903352338000000) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='BUSBAR', signal='U_MAG', wildcard={'BUSBAR': 'DCBB.8L2.R'}) \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count']).df\n\nu_mag_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nxcals_variable_name</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>0.002113</td>\n      <td>-0.975332</td>\n      <td>51951</td>\n      <td>-0.196706</td>\n      <td>0.375195</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n## 7.1.4. Circuit-Oriented Query - Polymorphism\n- Multiple circuit names\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_dfs = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(100, 's'), (100, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name=['RB.A12', 'RB.A45'], system='PC', signal='I_MEAS')\\\n    .signal_query().dfs\n\nax = i_meas_dfs[0].plot()\ni_meas_dfs[1].plot(ax=ax, grid=True)\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45531eff98>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_157_1.png)\n\n\n- Multiple system names\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_hts_dfs = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=1544622149598000000, duration=[(50, 's'), (150, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system=['LEADS_EVEN', 'LEADS_ODD'], signal='U_HTS') \\\n    .signal_query().dfs\n\nax = u_hts_dfs[0].plot()\nu_hts_dfs[1].plot(ax=ax)\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f4553148358>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_159_1.png)\n\n\n- Multiple signal names\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_ext_dfs = QueryBuilder().with_pm() \\\n    .with_timestamp(1544622149598000000) \\\n    .with_circuit_type('RQ') \\\n    .with_metadata(circuit_name='RQD.A12', system='QDS', signal=['U_1_EXT', 'U_2_EXT'],\n                        source='16L2', wildcard={'CELL': '16L2'}) \\\n    .signal_query().dfs\n\nax = u_ext_dfs[0].plot()\nu_ext_dfs[1].plot(ax=ax)\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45533a7710>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_161_1.png)\n\n\n- Signal wildcard\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\nimport matplotlib.pyplot as plt\nimport pytimber\nldb = pytimber.LoggingDB()\n\nu_diode_rqd_dfs = QueryBuilder().with_cals(ldb) \\\n    .with_duration(t_start=int(1544622149613000000), duration=[(50, 's'), (150, 's')]) \\\n    .with_circuit_type('RQ') \\\n    .with_metadata(circuit_name='RQD.A12', system='DIODE_RQD', signal='U_DIODE_RQD', wildcard={'MAGNET': '*'})\\\n    .signal_query().dfs\n\n# plot all\nfig, ax = plt.subplots()\nfor u_diode_rqd_df in u_diode_rqd_dfs:\n    u_diode_rqd_df.plot(ax=ax)\nax.legend().set_visible(False)\n```\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_163_0.png)\n\n\n## 7.1.5. Advanced Feature Query\n\nNXCALS enables calculation of signal features such as min, max, mean, std, count directly on the cluster without the need for costly query of the signal and performing calculation locally. This approach enables parallel computing on the cluster. To this end, a query should contain an element enabling a group by operation. Each group by operation allows for executing computation in parallel. For the sake of compactness, we only show examples for circuit-oriented query, however, the same principle applies to the general-purpose queries.\n- Feature query of multiple signals for the same period of time\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=1526898157236000000, t_end=1526903352338000000) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='*', system='PC', signal='I_MEAS') \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count']).df\n\ni_meas_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>device</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RPTE.UA63.RB.A56</td>\n      <td>10974.36</td>\n      <td>756.90</td>\n      <td>2498</td>\n      <td>5372.636369</td>\n      <td>3314.646638</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>RPTE.UA87.RB.A81</td>\n      <td>10973.21</td>\n      <td>756.82</td>\n      <td>2498</td>\n      <td>5371.596990</td>\n      <td>3314.230379</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>RPTE.UA83.RB.A78</td>\n      <td>10967.94</td>\n      <td>756.47</td>\n      <td>2498</td>\n      <td>5369.226934</td>\n      <td>3312.676833</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>RPTE.UA27.RB.A23</td>\n      <td>10976.46</td>\n      <td>757.04</td>\n      <td>2498</td>\n      <td>5373.973151</td>\n      <td>3315.324716</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>RPTE.UA43.RB.A34</td>\n      <td>10975.16</td>\n      <td>756.95</td>\n      <td>2497</td>\n      <td>5373.559131</td>\n      <td>3314.074552</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>RPTE.UA67.RB.A67</td>\n      <td>10978.56</td>\n      <td>757.18</td>\n      <td>2498</td>\n      <td>5374.329179</td>\n      <td>3315.855302</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>RPTE.UA47.RB.A45</td>\n      <td>10971.44</td>\n      <td>756.71</td>\n      <td>2497</td>\n      <td>5370.954557</td>\n      <td>3312.837184</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>RPTE.UA23.RB.A12</td>\n      <td>10978.80</td>\n      <td>757.18</td>\n      <td>2498</td>\n      <td>5374.768659</td>\n      <td>3315.972856</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- Feature query of multiple signals with the same period of time subdivided into three intervals - group by signal name and interval\n\n\n```python\nt_start_injs = 1526898157236000000\nt_end_injs = 1526899957236000000\nt_start_sbs = 1526901552338000000\nt_end_sbs = 1526903352338000000\n```\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start_injs, t_end=t_end_sbs) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='PC', signal='I_MEAS')\\\n    .signal_query().dfs[0]\n\nax = i_meas_df.plot(figsize=(10,5), linewidth=5)\nax.axvspan(xmin=t_start_injs, xmax=t_end_injs, facecolor='xkcd:goldenrod')\nax.axvspan(xmin=t_end_injs, xmax=t_start_sbs, facecolor='xkcd:grey')\nax.axvspan(xmin=t_start_sbs, xmax=t_end_sbs, facecolor='xkcd:green')\n```\n\n\n\n\n    <matplotlib.patches.Polygon at 0x7f45526d3518>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_168_1.png)\n\n\nFunction translate introduces a mapping based on the time column. Here, we consider three subintervals for beam injection, beam acceleration, and stable beams.\n\nAs a result, the time column forms a partition and can be executed in parallel.\n\n\n```python\nfrom pyspark.sql.functions import udf\nfrom pyspark.sql.types import IntegerType\n\ndef translate(timestamp):\n    if(timestamp >= t_start_injs and timestamp < t_end_injs):\n        return 1\n    \n    if(timestamp >=  t_end_injs and timestamp < t_start_sbs):\n        return 2\n    \n    if(timestamp >= t_start_sbs and timestamp <= t_end_sbs):\n        return 3\n    \n    return -1\n\ntranslate_udf = udf(translate, IntegerType())\n```\n\nThe translate function should be passed as a function argument\n\n\n```python\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start_injs, t_end=t_end_sbs) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='*', system='PC', signal='I_MEAS') \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count'], function=translate_udf).df\n\ni_meas_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>device</th>\n      <th>class</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RPTE.UA43.RB.A34</td>\n      <td>1</td>\n      <td>756.96</td>\n      <td>756.95</td>\n      <td>60</td>\n      <td>756.959833</td>\n      <td>0.001291</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>RPTE.UA63.RB.A56</td>\n      <td>3</td>\n      <td>10974.36</td>\n      <td>10974.35</td>\n      <td>60</td>\n      <td>10974.350167</td>\n      <td>0.001291</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>RPTE.UA47.RB.A45</td>\n      <td>3</td>\n      <td>10971.44</td>\n      <td>10971.43</td>\n      <td>60</td>\n      <td>10971.439667</td>\n      <td>0.001810</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>RPTE.UA47.RB.A45</td>\n      <td>2</td>\n      <td>10970.89</td>\n      <td>756.71</td>\n      <td>2377</td>\n      <td>5346.059971</td>\n      <td>3193.562789</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>RPTE.UA23.RB.A12</td>\n      <td>3</td>\n      <td>10978.80</td>\n      <td>10978.77</td>\n      <td>60</td>\n      <td>10978.781500</td>\n      <td>0.007552</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>RPTE.UA83.RB.A78</td>\n      <td>2</td>\n      <td>10967.54</td>\n      <td>756.47</td>\n      <td>2378</td>\n      <td>5344.349941</td>\n      <td>3193.572017</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>RPTE.UA43.RB.A34</td>\n      <td>2</td>\n      <td>10974.68</td>\n      <td>756.95</td>\n      <td>2377</td>\n      <td>5348.695949</td>\n      <td>3194.776339</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>RPTE.UA67.RB.A67</td>\n      <td>1</td>\n      <td>757.19</td>\n      <td>757.18</td>\n      <td>60</td>\n      <td>757.188833</td>\n      <td>0.003237</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>RPTE.UA87.RB.A81</td>\n      <td>3</td>\n      <td>10973.21</td>\n      <td>10973.20</td>\n      <td>60</td>\n      <td>10973.209167</td>\n      <td>0.002787</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>RPTE.UA27.RB.A23</td>\n      <td>2</td>\n      <td>10976.10</td>\n      <td>757.04</td>\n      <td>2378</td>\n      <td>5349.106434</td>\n      <td>3196.138736</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>RPTE.UA83.RB.A78</td>\n      <td>3</td>\n      <td>10967.94</td>\n      <td>10967.94</td>\n      <td>60</td>\n      <td>10967.940000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>RPTE.UA47.RB.A45</td>\n      <td>1</td>\n      <td>756.71</td>\n      <td>756.71</td>\n      <td>60</td>\n      <td>756.710000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>RPTE.UA83.RB.A78</td>\n      <td>1</td>\n      <td>756.48</td>\n      <td>756.47</td>\n      <td>60</td>\n      <td>756.472000</td>\n      <td>0.004034</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>RPTE.UA27.RB.A23</td>\n      <td>1</td>\n      <td>757.05</td>\n      <td>757.04</td>\n      <td>60</td>\n      <td>757.046833</td>\n      <td>0.004691</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>RPTE.UA87.RB.A81</td>\n      <td>2</td>\n      <td>10972.79</td>\n      <td>756.83</td>\n      <td>2378</td>\n      <td>5346.697637</td>\n      <td>3195.063002</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>RPTE.UA87.RB.A81</td>\n      <td>1</td>\n      <td>756.83</td>\n      <td>756.82</td>\n      <td>60</td>\n      <td>756.829167</td>\n      <td>0.002787</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>RPTE.UA23.RB.A12</td>\n      <td>2</td>\n      <td>10978.41</td>\n      <td>757.19</td>\n      <td>2378</td>\n      <td>5349.879361</td>\n      <td>3196.753501</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>RPTE.UA67.RB.A67</td>\n      <td>2</td>\n      <td>10978.15</td>\n      <td>757.18</td>\n      <td>2378</td>\n      <td>5349.423865</td>\n      <td>3196.631627</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>RPTE.UA63.RB.A56</td>\n      <td>2</td>\n      <td>10973.97</td>\n      <td>756.90</td>\n      <td>2378</td>\n      <td>5347.758797</td>\n      <td>3195.477077</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>RPTE.UA43.RB.A34</td>\n      <td>3</td>\n      <td>10975.16</td>\n      <td>10975.15</td>\n      <td>60</td>\n      <td>10975.154833</td>\n      <td>0.005039</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>RPTE.UA23.RB.A12</td>\n      <td>1</td>\n      <td>757.22</td>\n      <td>757.18</td>\n      <td>60</td>\n      <td>757.201667</td>\n      <td>0.007847</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>RPTE.UA27.RB.A23</td>\n      <td>3</td>\n      <td>10976.46</td>\n      <td>10976.45</td>\n      <td>60</td>\n      <td>10976.450333</td>\n      <td>0.001810</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>RPTE.UA63.RB.A56</td>\n      <td>1</td>\n      <td>756.91</td>\n      <td>756.90</td>\n      <td>60</td>\n      <td>756.903667</td>\n      <td>0.004860</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>RPTE.UA67.RB.A67</td>\n      <td>3</td>\n      <td>10978.56</td>\n      <td>10978.55</td>\n      <td>60</td>\n      <td>10978.550167</td>\n      <td>0.001291</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\nThe same method applied to NXCALS signals based on variable\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_mag_ab_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start_injs, t_end=t_end_sbs) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='BUSBAR', signal='U_MAG', wildcard={'BUSBAR': 'DCBB.8L2.R'}) \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count'], function=translate_udf).df\n\nu_mag_ab_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nxcals_variable_name</th>\n      <th>class</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>3</td>\n      <td>0.002003</td>\n      <td>-0.034035</td>\n      <td>18000</td>\n      <td>-0.001983</td>\n      <td>0.002882</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>1</td>\n      <td>0.002113</td>\n      <td>-0.005992</td>\n      <td>18000</td>\n      <td>-0.001861</td>\n      <td>0.002844</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>2</td>\n      <td>0.002041</td>\n      <td>-0.975332</td>\n      <td>15951</td>\n      <td>-0.636315</td>\n      <td>0.423767</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\nThis method can be used together with signal decimation, i.e., taking every nth sample.\n\nFor example this can be useful to query QPS board A and B which share the same channel and samples are shifted by 5 so that \n- every 10-th sample belongs to board A (or B), decimation=10\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_mag_a_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start_injs, t_end=t_end_sbs) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='BUSBAR', signal='U_MAG', wildcard={'BUSBAR': 'DCBB.8L2.R'}) \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count'], function=translate_udf, decimation=10).df\n\nu_mag_a_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nxcals_variable_name</th>\n      <th>class</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>3</td>\n      <td>0.002003</td>\n      <td>-0.012705</td>\n      <td>1800</td>\n      <td>0.000812</td>\n      <td>0.000529</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>1</td>\n      <td>0.002113</td>\n      <td>-0.000463</td>\n      <td>1800</td>\n      <td>0.000965</td>\n      <td>0.000434</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>2</td>\n      <td>0.002041</td>\n      <td>-0.969405</td>\n      <td>1595</td>\n      <td>-0.633601</td>\n      <td>0.423847</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- every 5+10-th sample belongs to board B (or A), decimation=10, shift=5\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_mag_b_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start_injs, t_end=t_end_sbs) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='BUSBAR', signal='U_MAG', wildcard={'BUSBAR': 'DCBB.8L2.R'}) \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count'], function=translate_udf, decimation=10, shift=5).df\n\nu_mag_b_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nxcals_variable_name</th>\n      <th>class</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>3</td>\n      <td>-0.003425</td>\n      <td>-0.034035</td>\n      <td>1800</td>\n      <td>-0.004754</td>\n      <td>0.000877</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>1</td>\n      <td>-0.003361</td>\n      <td>-0.005992</td>\n      <td>1800</td>\n      <td>-0.004656</td>\n      <td>0.000441</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>DCBB.8L2.R:U_MAG</td>\n      <td>2</td>\n      <td>-0.003052</td>\n      <td>-0.975332</td>\n      <td>1595</td>\n      <td>-0.639233</td>\n      <td>0.423941</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- with polymorphism one can query 1248 busbar at once (in two batches of 624 due to the limit of 1000 signal per query)\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nu_mag_ab_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start_injs, t_end=t_end_sbs) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='*', system='BUSBAR', signal='U_MAG', wildcard={'BUSBAR': '*'}) \\\n    .feature_query(['mean', 'std', 'max', 'min', 'count'], function=translate_udf).df\n\nu_mag_ab_df.head()\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nxcals_variable_name</th>\n      <th>class</th>\n      <th>max</th>\n      <th>min</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>DCBA.22L4.L:U_MAG</td>\n      <td>1</td>\n      <td>-0.000167</td>\n      <td>-0.002102</td>\n      <td>18000</td>\n      <td>-0.001110</td>\n      <td>0.000639</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>DCBA.14L4.R:U_MAG</td>\n      <td>1</td>\n      <td>0.001179</td>\n      <td>-0.000796</td>\n      <td>18000</td>\n      <td>0.000171</td>\n      <td>0.000603</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>DCBA.A28R2.L:U_MAG</td>\n      <td>3</td>\n      <td>0.045531</td>\n      <td>-0.003455</td>\n      <td>18000</td>\n      <td>-0.002131</td>\n      <td>0.001361</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>DCBB.B22L2.R:U_MAG</td>\n      <td>1</td>\n      <td>0.000341</td>\n      <td>-0.003088</td>\n      <td>18000</td>\n      <td>-0.001283</td>\n      <td>0.000557</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>DCBB.11L2.R:U_MAG</td>\n      <td>3</td>\n      <td>0.048651</td>\n      <td>-0.002121</td>\n      <td>18000</td>\n      <td>0.001457</td>\n      <td>0.002587</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n## 7.1.6. Processing Raw Signals\nOnce a signal is queried, one can perform some operations on each of them.  \nIn this case, the order of operations does not matter (but can be checked).  \n\n|Signal query|Signal processing|\n| ---------- | :-------------- |\n|{DB}.{DURATION}.{QUERY_PARAMETERS}.{QUERY}| |\n|{DB}.{DURATION}.{CIRCUIT_TYPE}.{METADATA}.{QUERY}| |\n|&nbsp;|.synchronize_time()|\n|&nbsp;|.convert_index_to_sec()|\n|&nbsp;|.create_col_from_index()|\n|&nbsp;|.filter_median()|\n|&nbsp;|.remove_values_for_time_less_than()|\n|&nbsp;|.remove_initial_offset()|\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start='2015-01-13 16:59:11+01:00', t_end='2015-01-13 17:15:46+01:00') \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='PC', signal='I_MEAS') \\\n    .signal_query() \\\n    .synchronize_time() \\\n    .convert_index_to_sec().dfs[0]\n\ni_meas_df.plot()\n```\n\n\n\n\n    <matplotlib.axes._subplots.AxesSubplot at 0x7f45528afda0>\n\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_182_1.png)\n\n\n## 7.1.7. Processing Raw Events\nFor PM event queries one can perform several operations on source, timestamp dataframe. \n\n|Event query|Event processing|\n| ---------- | :-------------- |\n|{DB}.{DURATION}.{QUERY_PARAMETERS}.{QUERY}| |\n|{DB}.{DURATION}.{CIRCUIT_TYPE}.{METADATA}.{QUERY}| |\n|&nbsp;|.filter_source()|\n|&nbsp;|.drop_duplicate_source()|\n|&nbsp;|.sort_values()|\n\nThe processing methods are dedicated to performing repeated operations on PM events. In case of searching a given system and className with wildcard '\\*' as a source, the event query can return events from different sectors. In this case, one can filter events to contain to a given sector. Some PM systems return duplicate events from different types of boards. In this case one can drop duplicate sources. Eventually, the events can be sorted by either source or timestamp.\n\n\n- Filter source\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(24*60*60, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='QH', source='*') \\\n    .event_query() \\\n    .df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B20L5</td>\n      <td>1426220469491000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C20L5</td>\n      <td>1426220517100000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A20L5</td>\n      <td>1426220518112000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A21L5</td>\n      <td>1426220625990000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B21L5</td>\n      <td>1426220866112000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>C23L4</td>\n      <td>1426236802332000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>B23L4</td>\n      <td>1426236839404000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A23L4</td>\n      <td>1426236839832000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>C22L4</td>\n      <td>1426236949841000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>C15R4</td>\n      <td>1426251285711000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>B15R4</td>\n      <td>1426251337747000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>A15R4</td>\n      <td>1426251388741000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>B34L8</td>\n      <td>1426258716281000000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>C34L8</td>\n      <td>1426258747672000000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>A34L8</td>\n      <td>1426258747370000000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>C33L8</td>\n      <td>1426258835955000000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>C34R7</td>\n      <td>1426258853947000000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>A34R7</td>\n      <td>1426258854113000000</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>A20R3</td>\n      <td>1426267931956000000</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>B20R3</td>\n      <td>1426267983579000000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>C20R3</td>\n      <td>1426268004144000000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>B18L5</td>\n      <td>1426277626360000000</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>A18L5</td>\n      <td>1426277679838000000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>C18L5</td>\n      <td>1426277680496000000</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>A19L5</td>\n      <td>1426277903449000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\nExecuting filter_source() with circuit name and system type would filter out events not belonging to a given circuit name\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(24*60*60, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='QH', source='*') \\\n    .event_query() \\\n    .filter_source('RB.A45', 'QH') \\\n    .df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B20L5</td>\n      <td>1426220469491000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C20L5</td>\n      <td>1426220517100000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A20L5</td>\n      <td>1426220518112000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A21L5</td>\n      <td>1426220625990000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B21L5</td>\n      <td>1426220866112000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>C15R4</td>\n      <td>1426251285711000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>B15R4</td>\n      <td>1426251337747000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A15R4</td>\n      <td>1426251388741000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>B18L5</td>\n      <td>1426277626360000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>A18L5</td>\n      <td>1426277679838000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>C18L5</td>\n      <td>1426277680496000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>A19L5</td>\n      <td>1426277903449000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- Drop duplicates\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(24*60*60, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='QDS', source='*') \\\n    .event_query() \\\n    .df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B20L5</td>\n      <td>1426220469490000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C20L5</td>\n      <td>1426220517099000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A20L5</td>\n      <td>1426220518111000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A21L5</td>\n      <td>1426220625989000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B21L5</td>\n      <td>1426220866111000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>B20L5</td>\n      <td>1426220469492000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>C20L5</td>\n      <td>1426220517101000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A20L5</td>\n      <td>1426220518113000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>A21L5</td>\n      <td>1426220625991000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>B21L5</td>\n      <td>1426220866113000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>C23L4</td>\n      <td>1426236802331000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>B23L4</td>\n      <td>1426236839403000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>A23L4</td>\n      <td>1426236839831000000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>C22L4</td>\n      <td>1426236949840000000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>C23L4</td>\n      <td>1426236802333000000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>B23L4</td>\n      <td>1426236839405000000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>A23L4</td>\n      <td>1426236839833000000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>C22L4</td>\n      <td>1426236949842000000</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>C15R4</td>\n      <td>1426251285710000000</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>B15R4</td>\n      <td>1426251337746000000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>A15R4</td>\n      <td>1426251388740000000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>C15R4</td>\n      <td>1426251285712000000</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>B15R4</td>\n      <td>1426251337748000000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>A15R4</td>\n      <td>1426251388742000000</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>B34L8</td>\n      <td>1426258716280000000</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>C34L8</td>\n      <td>1426258747671000000</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>A34L8</td>\n      <td>1426258747369000000</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>C33L8</td>\n      <td>1426258835954000000</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>C34R7</td>\n      <td>1426258853946000000</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>A34R7</td>\n      <td>1426258854112000000</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>B34L8</td>\n      <td>1426258716282000000</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>C34L8</td>\n      <td>1426258747673000000</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>A34L8</td>\n      <td>1426258747371000000</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>C33L8</td>\n      <td>1426258835956000000</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>C34R7</td>\n      <td>1426258853948000000</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>A34R7</td>\n      <td>1426258854114000000</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>A20R3</td>\n      <td>1426267931955000000</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>B20R3</td>\n      <td>1426267983578000000</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>C20R3</td>\n      <td>1426268004143000000</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>A20R3</td>\n      <td>1426267931957000000</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>B20R3</td>\n      <td>1426267983580000000</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>C20R3</td>\n      <td>1426268004145000000</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>B18L5</td>\n      <td>1426277626359000000</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>A18L5</td>\n      <td>1426277679837000000</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>C18L5</td>\n      <td>1426277680495000000</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>A19L5</td>\n      <td>1426277903448000000</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>B18L5</td>\n      <td>1426277626361000000</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>A18L5</td>\n      <td>1426277679839000000</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>C18L5</td>\n      <td>1426277680497000000</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>A19L5</td>\n      <td>1426277903450000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\nExecuting filter_source() with circuit name and system type would filter out events not belonging to a given circuit name\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(24*60*60, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='QDS', source='*') \\\n    .event_query() \\\n    .filter_source('RB.A45', 'QDS') \\\n    .df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B20L5</td>\n      <td>1426220469490000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C20L5</td>\n      <td>1426220517099000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A20L5</td>\n      <td>1426220518111000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A21L5</td>\n      <td>1426220625989000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B21L5</td>\n      <td>1426220866111000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>B20L5</td>\n      <td>1426220469492000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>C20L5</td>\n      <td>1426220517101000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A20L5</td>\n      <td>1426220518113000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>A21L5</td>\n      <td>1426220625991000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>B21L5</td>\n      <td>1426220866113000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>C15R4</td>\n      <td>1426251285710000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>B15R4</td>\n      <td>1426251337746000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>A15R4</td>\n      <td>1426251388740000000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>C15R4</td>\n      <td>1426251285712000000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>B15R4</td>\n      <td>1426251337748000000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>A15R4</td>\n      <td>1426251388742000000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>B18L5</td>\n      <td>1426277626359000000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>A18L5</td>\n      <td>1426277679837000000</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>C18L5</td>\n      <td>1426277680495000000</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>A19L5</td>\n      <td>1426277903448000000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>B18L5</td>\n      <td>1426277626361000000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>A18L5</td>\n      <td>1426277679839000000</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>C18L5</td>\n      <td>1426277680497000000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>A19L5</td>\n      <td>1426277903450000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- drop_duplicate_source()\n\nSome PM systems return duplicate events from different types of boards. In this case one can drop duplicate sources. \n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(24*60*60, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='QDS', source='*') \\\n    .event_query() \\\n    .filter_source('RB.A45', 'QDS') \\\n    .drop_duplicate_source() \\\n    .df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B20L5</td>\n      <td>1426220469490000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C20L5</td>\n      <td>1426220517099000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A20L5</td>\n      <td>1426220518111000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A21L5</td>\n      <td>1426220625989000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B21L5</td>\n      <td>1426220866111000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>C15R4</td>\n      <td>1426251285710000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>B15R4</td>\n      <td>1426251337746000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A15R4</td>\n      <td>1426251388740000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>B18L5</td>\n      <td>1426277626359000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>A18L5</td>\n      <td>1426277679837000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>C18L5</td>\n      <td>1426277680495000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>A19L5</td>\n      <td>1426277903448000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n- sort_values()\n\nThe events can be sorted by either source or timestamp.\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\n\nsource_timestamp_df = QueryBuilder().with_pm() \\\n    .with_duration(t_start='2015-03-13 05:20:59.4910002', duration=[(24*60*60, 's')]) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A45', system='QDS', source='*') \\\n    .event_query() \\\n    .filter_source('RB.A45', 'QDS') \\\n    .drop_duplicate_source() \\\n    .sort_values(by='timestamp') \\\n    .df\n\nsource_timestamp_df\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>B20L5</td>\n      <td>1426220469490000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C20L5</td>\n      <td>1426220517099000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A20L5</td>\n      <td>1426220518111000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A21L5</td>\n      <td>1426220625989000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B21L5</td>\n      <td>1426220866111000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>C15R4</td>\n      <td>1426251285710000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>B15R4</td>\n      <td>1426251337746000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>A15R4</td>\n      <td>1426251388740000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>B18L5</td>\n      <td>1426277626359000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>A18L5</td>\n      <td>1426277679837000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>C18L5</td>\n      <td>1426277680495000000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>A19L5</td>\n      <td>1426277903448000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n## 7.2. AssertionBuilder()\n\n<center>{SIGNALS}.(TIME_RANGE).{ASSERTION}\n\n|Signal input|Time range definition (optional) / Signal assertion|Signal assertions (if time range defined)|\n| ---------- | :------------------ |-----------------|\n|.with_signal()|&nbsp;|&nbsp;|\n|&nbsp;|.has_min_max_value()|&nbsp;|\n|&nbsp;|.compare_to_reference()|&nbsp;|\n|&nbsp;|.with_time_range()|.has_min_max_variation()|\n|&nbsp;|.with_time_range()|.has_min_max_slope()|\n\n- has_min_max_value()\n<img src=\"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/MP3.png\">\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\nfrom lhcsmapi.pyedsl.AssertionBuilder import AssertionBuilder\n\ntt891a_dfs = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start='2014-12-13 09:12:41+01:00', t_end='2014-12-13 12:27:11+01:00') \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system=['LEADS_EVEN', 'LEADS_ODD'], signal='TT891A') \\\n    .signal_query() \\\n    .synchronize_time() \\\n    .convert_index_to_sec() \\\n    .filter_median().dfs\n\n\nAssertionBuilder().with_signal(tt891a_dfs) \\\n    .has_min_max_value(value_min=46, value_max=54)\n```\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_198_0.png)\n\n\nIn the case below, the assertion should fail and raise a warning.\n\n\n```python\nAssertionBuilder().with_signal(tt891a_dfs) \\\n    .has_min_max_value(value_min=50, value_max=54)\n```\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:63: UserWarning: DACA05_07L2_TT891A.TEMPERATURECALC outside of the [50, 54] mV threshold\n      warnings.warn('{} outside of the [{}, {}] mV threshold'.format(col, value_min, value_max))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:63: UserWarning: DACA06_07L2_TT891A.TEMPERATURECALC outside of the [50, 54] mV threshold\n      warnings.warn('{} outside of the [{}, {}] mV threshold'.format(col, value_min, value_max))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:63: UserWarning: DABA01_07R1_TT891A.TEMPERATURECALC outside of the [50, 54] mV threshold\n      warnings.warn('{} outside of the [{}, {}] mV threshold'.format(col, value_min, value_max))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:63: UserWarning: DABA02_07R1_TT891A.TEMPERATURECALC outside of the [50, 54] mV threshold\n      warnings.warn('{} outside of the [{}, {}] mV threshold'.format(col, value_min, value_max))\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_200_1.png)\n\n\n- compare_to_reference()\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\nfrom lhcsmapi.pyedsl.AssertionBuilder import AssertionBuilder\nfrom lhcsmapi.reference.Reference import Reference\nfrom lhcsmapi.Time import Time\n\ntimestamp_ee_rqd = 1544622149701000000\ntimestamp_fgc_rqd = 1544622149620000000\nsignal_names = 'T_RES'\n\n\nt_res_df = QueryBuilder().with_pm() \\\n    .with_timestamp(timestamp_ee_rqd) \\\n    .with_circuit_type('RQ') \\\n    .with_metadata(circuit_name='RQD.A12', system='EE', signal=signal_names).signal_query() \\\n    .remove_values_for_time_less_than(timestamp_ee_rqd) \\\n    .synchronize_time(timestamp_fgc_rqd) \\\n    .convert_index_to_sec().dfs[0]\n\n\ntimestamp_ee_ref_rqd = Reference.get_power_converter_reference_fpa('RQ', 'RQD.A12', 'eePm')\ntimestamp_ee_ref_rqd = Time.to_unix_timestamp(timestamp_ee_ref_rqd)\n\ntimestamp_fgc_ref_rqd = Reference.get_power_converter_reference_fpa('RQ', 'RQD.A12', 'fgcPm')\ntimestamp_fgc_ref_rqd = Time.to_unix_timestamp(timestamp_fgc_ref_rqd)\n\n\nt_res_ref_df = QueryBuilder().with_pm() \\\n    .with_timestamp(timestamp_ee_ref_rqd) \\\n    .with_circuit_type('RQ') \\\n    .with_metadata(circuit_name='RQD.A12', system='EE', signal=signal_names).signal_query() \\\n    .remove_values_for_time_less_than(timestamp_ee_ref_rqd) \\\n    .synchronize_time(timestamp_fgc_ref_rqd) \\\n    .convert_index_to_sec().dfs[0]\n\nAssertionBuilder().with_signal([t_res_df])\\\n    .compare_to_reference(signal_ref_dfs=[t_res_ref_df], abs_margin=25, scaling=1)\n```\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_202_0.png)\n\n\n- has_min_max_variation()\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\nfrom lhcsmapi.pyedsl.AssertionBuilder import AssertionBuilder\nfrom lhcsmapi.analysis.busbar.BusbarResistanceAnalysis import BusbarResistanceAnalysis\n\nt_start = '2014-12-13 09:12:41+01:00'\nt_end = '2014-12-13 12:27:11+01:00'\n\ncv891_dfs = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start, t_end=t_end) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system=['LEADS_EVEN', 'LEADS_ODD'], signal='CV891') \\\n    .signal_query() \\\n    .synchronize_time() \\\n    .convert_index_to_sec() \\\n    .filter_median() \\\n    .dfs\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start, t_end=t_end) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='PC', signal='I_MEAS') \\\n    .signal_query() \\\n    .create_col_from_index('acqStamp') \\\n    .synchronize_time() \\\n    .convert_index_to_sec() \\\n    .dfs[0]\n\nplateau_start, plateau_end = BusbarResistanceAnalysis.get_current_plateau_start_end(i_meas_df, 'I_MEAS', i_meas_threshold=500)\nplateau_start_sync = [(ps-i_meas_df['acqStamp'].values[0])/1e9 for ps in plateau_start]\nplateau_end_sync = [(pe-i_meas_df['acqStamp'].values[0])/1e9 for pe in plateau_end]\n\nAssertionBuilder().with_signal(cv891_dfs) \\\n    .with_time_range(t_start=plateau_start_sync, t_end=plateau_end_sync) \\\n    .has_min_max_variation(variation_min_max=8)\n```\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_204_0.png)\n\n\nIn the case below, the variation is too tight and the assertion fails \n\n\n```python\nAssertionBuilder().with_signal(cv891_dfs) \\\n    .with_time_range(t_start=plateau_start_sync, t_end=plateau_end_sync) \\\n    .has_min_max_variation(variation_min_max=1)\n```\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:134: UserWarning: The variation of DACA05_07L2_CV891.POSST (1.0 %) exceeds 1.5 % for constant current from 806.0 to 5882.5 s\n      .format(col, variation_min_max, variation.values[0], t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:134: UserWarning: The variation of DACA06_07L2_CV891.POSST (1.0 %) exceeds 1.9000000000000021 % for constant current from 806.0 to 5882.5 s\n      .format(col, variation_min_max, variation.values[0], t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:134: UserWarning: The variation of DABA01_07R1_CV891.POSST (1.0 %) exceeds 1.6999999999999993 % for constant current from 806.0 to 5882.5 s\n      .format(col, variation_min_max, variation.values[0], t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:134: UserWarning: The variation of DABA01_07R1_CV891.POSST (1.0 %) exceeds 1.1000000000000014 % for constant current from 6199.0 to 9745.0 s\n      .format(col, variation_min_max, variation.values[0], t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:134: UserWarning: The variation of DABA02_07R1_CV891.POSST (1.0 %) exceeds 2.099999999999998 % for constant current from 806.0 to 5882.5 s\n      .format(col, variation_min_max, variation.values[0], t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:134: UserWarning: The variation of DABA02_07R1_CV891.POSST (1.0 %) exceeds 1.2000000000000028 % for constant current from 6199.0 to 9745.0 s\n      .format(col, variation_min_max, variation.values[0], t_s, t_e))\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_206_1.png)\n\n\n- has_min_max_slope()\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\nfrom lhcsmapi.pyedsl.AssertionBuilder import AssertionBuilder\nfrom lhcsmapi.analysis.busbar.BusbarResistanceAnalysis import BusbarResistanceAnalysis\n\nt_start = '2014-12-13 09:12:41+01:00'\nt_end = '2014-12-13 12:27:11+01:00'\n\nu_res_dfs = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start, t_end=t_end) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system=['LEADS_EVEN', 'LEADS_ODD'], signal='U_RES') \\\n    .signal_query() \\\n    .synchronize_time() \\\n    .convert_index_to_sec() \\\n    .filter_median() \\\n    .dfs\n\ni_meas_df = QueryBuilder().with_nxcals(spark) \\\n    .with_duration(t_start=t_start, t_end=t_end) \\\n    .with_circuit_type('RB') \\\n    .with_metadata(circuit_name='RB.A12', system='PC', signal='I_MEAS') \\\n    .signal_query() \\\n    .create_col_from_index('acqStamp') \\\n    .synchronize_time() \\\n    .convert_index_to_sec() \\\n    .dfs[0]\n\nplateau_start, plateau_end = BusbarResistanceAnalysis.get_current_plateau_start_end(i_meas_df, 'I_MEAS', i_meas_threshold=500)\nplateau_start_sync = [(ps-i_meas_df['acqStamp'].values[0])/1e9 for ps in plateau_start]\nplateau_end_sync = [(pe-i_meas_df['acqStamp'].values[0])/1e9 for pe in plateau_end]\n\nAssertionBuilder().with_signal(u_res_dfs) \\\n    .with_time_range(t_start=plateau_start_sync, t_end=plateau_end_sync) \\\n    .has_min_max_slope(slope_min=-2, slope_max=2, ylabel='U_RES')\n```\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_208_0.png)\n\n\nIn the case below, the slope is too tight and the assertion fails \n\n\n```python\nAssertionBuilder().with_signal(u_res_dfs) \\\n    .with_time_range(t_start=plateau_start_sync, t_end=plateau_end_sync) \\\n    .has_min_max_slope(slope_min=-2e-3, slope_max=2e-3, ylabel='U_RES')\n```\n\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:152: UserWarning: The drift of DFLAS.7L2.RB.A12.LD1:U_RES is -0.119 mV/h for constant current from 806.0 to 5882.5 s\n      .format(col, slope, unit, t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:152: UserWarning: The drift of DFLAS.7L2.RB.A12.LD1:U_RES is -0.667 mV/h for constant current from 6199.0 to 9745.0 s\n      .format(col, slope, unit, t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:152: UserWarning: The drift of DFLAS.7L2.RB.A12.LD2:U_RES is 0.159 mV/h for constant current from 806.0 to 5882.5 s\n      .format(col, slope, unit, t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:152: UserWarning: The drift of DFLAS.7L2.RB.A12.LD2:U_RES is 0.636 mV/h for constant current from 6199.0 to 9745.0 s\n      .format(col, slope, unit, t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:152: UserWarning: The drift of DFLAS.7R1.RB.A12.LD3:U_RES is 0.205 mV/h for constant current from 806.0 to 5882.5 s\n      .format(col, slope, unit, t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:152: UserWarning: The drift of DFLAS.7R1.RB.A12.LD3:U_RES is 0.421 mV/h for constant current from 6199.0 to 9745.0 s\n      .format(col, slope, unit, t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:152: UserWarning: The drift of DFLAS.7R1.RB.A12.LD4:U_RES is -0.188 mV/h for constant current from 806.0 to 5882.5 s\n      .format(col, slope, unit, t_s, t_e))\n    /eos/home-m/mmacieje/SWAN_projects/lhc-sm-api/lhcsmapi/pyedsl/AssertionBuilder.py:152: UserWarning: The drift of DFLAS.7R1.RB.A12.LD4:U_RES is -0.469 mV/h for constant current from 6199.0 to 9745.0 s\n      .format(col, slope, unit, t_s, t_e))\n\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_210_1.png)\n\n\n## 7.3. FeatureBuilder()\n\n<center>{SIGNALS}.(FEATURE_CALCULATION).{ASSERTION}</center>\n\ne.g.  \n```python\nFeatureBuilder().with_signal(u_hds_dfs) \\\n                .calculate_features(features=['first', 'last20mean', 'tau_charge'], index=1544622149599000000)\n```\nSupported functions are: \n\n```python\n['first', 'first20mean', 'last', 'last20mean', 'max', 'min', 'median', 'std', 'mean', 'tau_charge', 'tau_energy', 'tau_lin_reg', 'tau_exp_fit']\n```\nFor example, to calculate initial voltage, final mean voltage based on the last 20 points, and the characteristic time of the pseudo-exponential decay for a quench heater voltage.\n<img src = \"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/QH.png\">\n\n\n```python\nfrom lhcsmapi.pyedsl.QueryBuilder import QueryBuilder\nfrom lhcsmapi.pyedsl.FeatureBuilder import FeatureBuilder\nimport matplotlib.pyplot as plt\n\ntimestamp = 1544622149599000000\n\nu_hds_dfs = QueryBuilder().with_pm() \\\n    .with_timestamp(timestamp) \\\n    .with_circuit_type('RQ') \\\n    .with_metadata(circuit_name='RQD.A12', system='QH', signal='U_HDS', source='16L2', wildcard={'CELL': '16L2'}) \\\n    .signal_query()\\\n    .synchronize_time(timestamp)\\\n    .convert_index_to_sec().dfs\n\nax = u_hds_dfs[0].plot(figsize=(15,7))\nu_hds_dfs[1].plot(ax=ax, grid=True)\nplt.show()\n\nFeatureBuilder().with_signal(u_hds_dfs) \\\n                .calculate_features(features=['first', 'last20mean', 'tau_charge'], index=1544622149599000000)\n```\n\n\n![png](https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/output/output_212_0.png)\n\n\n\n\n\n<div>\n\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>16L2:U_HDS_1:first</th>\n      <th>16L2:U_HDS_1:last20mean</th>\n      <th>16L2:U_HDS_1:tau_charge</th>\n      <th>16L2:U_HDS_2:first</th>\n      <th>16L2:U_HDS_2:last20mean</th>\n      <th>16L2:U_HDS_2:tau_charge</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1544622149599000000</th>\n      <td>880.4621</td>\n      <td>5.575086</td>\n      <td>0.07779</td>\n      <td>872.8354</td>\n      <td>6.734703</td>\n      <td>0.076704</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n# Known Issues\nIn order to update the lhcsmapi package one has to execute the command\n```\npip install --user --update lhcsmapi\n```\ntwice while using the SWAN terminal (cf. an error message in the figure below).\n<img src=\"https://gitlab.cern.ch/LHCData/lhc-sm-api/raw/master/figures/double_reinstallation_error.png\">",
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