def plot(name, data):
filename = os.path.join(CSV_DIR, "{name}_{type}.csv".format(name=name, type=get_datapoint_type()))
headers = list(data.keys())
amount_of_rows = max(map(len, data.values()))
rows = [None] * amount_of_rows
for rownr in range(amount_of_rows):
rows[rownr] = {daynr: get_datapoint(data, daynr, rownr) for daynr in headers}
csvfp = csv.DictWriter(open(filename, "w"), fieldnames=headers)
csvfp.writeheader()
csvfp.writerows(rows)
if __name__ == '__main__':
type = get_datapoint_type()
if type == DAS:
aggregator = aggregate_das
elif type == GAP:
aggregator = aggregate_gap
elif type == CTIME:
aggregator = aggregate_ctime
else:
assert False, "Not possible :)"
analyse(chain(*relational.BROTHERS.items()), aggregator=aggregator, plotter=plot, plot_threaded=True)
median, stderr = bootstrapped[day]
except KeyError:
# This bird didn't have this day
yield ""
else:
yield "{median}±{stderr}".format(**locals())
def plot(*veugel_tuples):
all_veugels = list(itertools.chain(*veugel_tuples))
# Determine all available days
all_days = set()
for (name, bootstrapped) in all_veugels:
for daynr in bootstrapped.keys():
all_days.add(daynr)
all_days = list(sorted(all_days))
# Open CSV file
filename = os.path.join(CSV_DIR, "bootstrapped.csv")
file = open(filename, "w")
csvfp = csv.writer(file)
csvfp.writerow(["name"] + all_days)
for name, bootstrapped in all_veugels:
csvfp.writerow([name] + list(get_row(bootstrapped, all_days)))
if __name__ == '__main__':
analyse(relational.BROTHERS.items(), aggregator=aggregate, plotter=plot, plot_threaded=False)
# peaks: een dictionary van bucket (int) -> count (int)
# buckets: een dictionary van bucket (int) -> count(int)
# Return an ordered dict of buckets, and their peaks
return buckets, peaks
def aggregate(veugel):
# @LINDA: Kies je dagen die je wil berekenen in de onderstaande constructie!
days = veugel.days # FILTER DUS HIER
aggregated = [(day.daynr, day_aggregate(day.rows["duration_of_state"])) for day in days]
aggregated = OrderedDict(aggregated)
# HACK: als we hier een enkel item teruggeven gaat analyse() over de zeik :<. We geven
# dus een lijst met een enkel item terug, wat vervolgens weer goed gaat in plot().
return [aggregated]
def plot(aggregated):
pyplot.figure()
for day, (buckets, peaks) in aggregated.items():
print(day)
pyplot.plot(list(buckets.keys()), list(buckets.values()))
pyplot.plot(list(peaks.keys()), list(peaks.values()), 'x')
pyplot.show()
if __name__ == "__main__":
analyse([list(BROTHERS.keys())[0]], aggregator=aggregate, plotter=plot, plot_threaded=True)
#print(sorted(Counter(choose_day(iso.days, daynr).rows["duration_of_state"]).items()))
#print(sorted(Counter(choose_day(self.days, daynr).rows["duration_of_state"]).items()))
iso_das = choose_day(iso.days, daynr).rows["duration_of_state"]
self_das = choose_day(self.days, daynr).rows["duration_of_state"]
yield stats.ks_2samp(iso_das, self_das)
def _aggregate(iso, self):
yield iso.name
yield self.name
yield get_hist_data(iso)
yield get_hist_data(self)
yield list(get_ks(iso, self))
def aggregate(iso, self):
return list(_aggregate(iso, self))
def plot(iso_name, self_name, iso_hist, self_hist, ks):
print("{} <-> {}".format(iso_name, self_name))
for day, res in zip(BUCKETS, ks):
print("{}: {}".format(day, res))
if __name__ == '__main__':
analyse(list(relational.BROTHERS.items())[:1], plot, aggregate, plot_threaded=True)
from veugel import relational
from veugel.analyse import analyse
def plot_avg_das(means):
pyplot.plot(list(means.keys()), list(means.values()), marker='x')
def plot(iso_name, iso_avgs, self_name, self_avgs):
pyplot.figure()
pyplot.gca().set_color_cycle(['red', 'blue'])
plot_avg_das(iso_avgs)
plot_avg_das(self_avgs)
pyplot.ylabel('Mean DAS')
pyplot.xlabel('DPH')
pyplot.ylim(0, 150)
pyplot.legend([iso_name, self_name], loc=0)
pyplot.savefig("das_{iso_name}_{self_name}.png".format(**locals()), bbox_inches='tight')
def get_das_mapping(veugel):
return OrderedDict((day.daynr, day.get_das_mean()) for day in veugel.days)
def aggregate(iso, self):
return [iso.name, get_das_mapping(iso), self.name, get_das_mapping(self)]
if __name__ == '__main__':
analyse(relational.BROTHERS.items(), plot, aggregate, plot_threaded=True)
