Executing this Notebook requires a personal STOQS server. Follow the steps to build your own development system — this will take a few hours and depends on a good connection to the Internet. Once your server is up log into it (after a cd ~/Vagrants/stoqsvm) and activate your virtual environment with the usual commands:
vagrant ssh -- -X
cd /vagrant/dev/stoqsgit
source venv-stoqs/bin/activate
Connect to your Institution's STOQS database server using read-only credentials. (Note: firewalls typically limit unprivileged access to such resources.)
cd stoqs
ln -s mbari_campaigns.py campaigns.py
export DATABASE_URL=postgis://everyone:guest@kraken.shore.mbari.org:5433/stoqs
Launch Jupyter Notebook on your system with:
cd contrib/notebooks
../../manage.py shell_plus --notebook
navigate to this file and open it. You will then be able to execute the cells and experiment with this notebook.
For the KISS April 2017 campaign add up the hours and collect in a dictionary all the platform start and end times using Matplotlib mdate format:
In [1]:
from collections import defaultdict
from datetime import timedelta
import matplotlib.dates as mdates
import operator
db = 'stoqs_canon_april2017'
plat_start_ends = defaultdict(list)
plat_depl_dur = {}
print('Platform Name # Deployments Hours')
print('------------- ------------- -----')
for plat in Platform.objects.using(db).all():
time_diff_sum = timedelta(0)
for act in Activity.objects.using(db).filter(platform=plat):
time_diff = act.enddate - act.startdate
time_diff_sum += time_diff
plat_start_ends[plat].append((mdates.date2num(act.startdate),
mdates.date2num(act.enddate)))
plat_depl_dur[plat] = (len(plat_start_ends[plat]),
time_diff_sum.total_seconds() / 3600)
print(f'{plat.name:20s} {plat_depl_dur[plat][0]:10d} {plat_depl_dur[plat][1]:7.1f}')
Activity start and end date values may be incorrect in legacy databases. To correct them this script can be run by the database admininstrator to update them based on the actual InstantPoint timevalues:
db = 'stoqs_canon_april2017'
for act in Activity.objects.using(db):
ip_qs = (InstantPoint.objects.using(db)
.filter(activity=act)
.aggregate(Max('timevalue'), Min('timevalue')))
if ip_qs['timevalue__min'] and ip_qs['timevalue__max']:
act.startdate = ip_qs['timevalue__min']
act.enddate = ip_qs['timevalue__max']
act.save(using=db)
else:
act.delete(using=db)
In [2]:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import pylab
from numpy import arange
pylab.rcParams['figure.figsize'] = (15, 9);
fig, ax = plt.subplots()
plat_labels = []
# Annotate at max x value and plot in order by platformtype name
xpos = max([dr[1] for dr in list(plat_start_ends.values())[0]])
for ypos, plat in enumerate(
sorted(plat_start_ends.keys(),
key=operator.attrgetter('platformtype.name'))):
plat_labels.append(f'{plat.name} ({plat.platformtype.name})')
for bdate, edate in plat_start_ends[plat]:
ax.barh(ypos+0.5, edate - bdate, left=bdate, height=0.8,
align='center', color='#' + plat.color, alpha=0.5)
if plat_depl_dur[plat][0] == 1:
fmt = '{:10d} deployment {:7.1f} hours'
else:
fmt = '{:10d} deployments {:7.1f} hours'
ax.annotate(fmt.format(plat_depl_dur[plat][0], plat_depl_dur[plat][1]),
verticalalignment='center', horizontalalignment='right',
xy=(xpos, ypos+0.5), fontsize=13, )
ax.set_title(Campaign.objects.using(db).get(id=1).description)
ax.set_ylim(-0.5, len(plat_labels) + 0.5)
ax.set_yticks(arange(len(plat_labels)) + 0.5)
ax.set_yticklabels(plat_labels)
ax.grid(True)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))
plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MO))
plt.gca().xaxis.set_minor_locator(mdates.DayLocator())
plt.gcf().autofmt_xdate()
plt.show()
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