LHC Luminosity¶

---

Extract live data from the LHC and create interactive plots with PyTimber.

Preparation¶

Let's import all the Python modules necessary for this study and tell matplotlib to generate interactive javascript plots.

In [18]:

# Enable an interactive mode for Matplotlib plots
%matplotlib notebook

from datetime import datetime

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import time
import pytz
import pytimber

We now open the connection to the database:

In [2]:

db = pytimber.LoggingDB(spark_session=spark)

Retrieve the data from the logging database¶

We are now ready to open a connection to the logging database and extract the luminosity for the past 24 hours.

In [3]:

now = time.time()
now_minus_a_day = now - 3600 * 24

t1 = "2016-08-03 14:00:00"
t2 = "2016-08-04 12:00:00"

# Uncomment for recent data
# t1 = now
# t2 = now_minus_a_day

alice = 'ALICE:LUMI_TOT_INST'
atlas = 'ATLAS:LUMI_TOT_INST'
cms = 'CMS:LUMI_TOT_INST'
lhcb = 'LHCB:LUMI_TOT_INST'
data = db.get([alice, atlas, cms, lhcb], t1, t2)

Plotting the data¶

We can now build a plot of the intensity and beams energy starting from the data we retrieved. The matplotlib library can be used for creating interactive javascript based data visualisations.

In [19]:

def dumpdate(t=None, fmt="%Y-%m-%d %H:%M:%S.SSS"):
    utc_dt = datetime.utcfromtimestamp(t)
    tz = pytz.timezone("Europe/Zurich")
    tz_dt = utc_dt.astimezone(tz)
    return tz_dt.strftime(fmt)


class rdmDateFormatter(ticker.Formatter):
    def __call__(self, x, pos=None):
        return dumpdate(x, fmt="%Y-%m-%d\n%H:%M:%S")


# Create figure
plt.figure(figsize=(12, 6))

# Plot Alice
tt, vv = data[alice]
plt.plot(tt, 1000 * vv, '-g', label=r'Alice $\times$ 1000')

# Plot Atlas
tt, vv = data[atlas]
plt.plot(tt, vv, '-b', label='Atlas')

# Plot CMS
tt, vv = data[cms]
plt.plot(tt, vv, '-r', label='CMS')

# Plot LHCb
tt, vv = data[lhcb]
plt.plot(tt, 10 * vv, '-k', label=r'LHCb $\times$ 10')

# Set axis and legend
plt.ylabel(r'Luminosity [$10^{30} \rm cm^{-2}  s^{-1}$]')
plt.legend()
plt.title(time.asctime(time.localtime(now)))

# set xaxis date
ax = plt.gca()
ax.xaxis.set_major_formatter(rdmDateFormatter())
ax.xaxis.major.locator._nbins = 6
plt.draw()