def zoomout_query(query_results, resolution):
"""
this is trying to decrease the time resolution of desc ordered query
results with datetime objects to the specified resolution, which should
be a datetime.timedelta object;
for each item, the index number of the datetime object must be 1;
the non-datetime column will be normalized by the value of norm to a percentage
"""
zoomouted = []
last_index = len(query_results) - 1
# starting from the second item in query_results, since the first and last
# one will be used to indicate the actually timestamp, shouldn't be
# averaged
beg_item = query_results[1]
ref_t = beg_item[1]
# ts: used to collect time for average;
# cs for cores initial reference time
cs, ts = [beg_item[0]], [util.dat2time(ref_t)]
for k, item in enumerate(query_results):
item_c, item_t = item
item_c = item_c
if k == 0 or k == last_index: # not do anything with the first and last item
item = list(item) # tuple => list
zoomouted.append([item_c, item_t])
else:
if item_t - ref_t <= resolution:
ts.append(util.dat2time(item_t))
cs.append(item_c)
else:
zoomouted.append([
np.average(cs),
datetime.datetime.fromtimestamp(np.average(ts))
])
ref_t = item_t
cs, ts = [item_c], [util.dat2time(item_t)
] # reinitialize ts, cs
return zoomouted
def zoomout_query(query_results, resolution):
"""
this is trying to decrease the time resolution of desc ordered query
results with datetime objects to the specified resolution, which should
be a datetime.timedelta object;
for each item, the index number of the datetime object must be 1;
the non-datetime column will be normalized by the value of norm to a percentage
"""
zoomouted = []
last_index = len(query_results) - 1
# starting from the second item in query_results, since the first and last
# one will be used to indicate the actually timestamp, shouldn't be
# averaged
beg_item = query_results[1]
ref_t = beg_item[1]
# ts: used to collect time for average;
# cs for cores initial reference time
cs, ts = [beg_item[0]], [util.dat2time(ref_t)]
for k, item in enumerate(query_results):
item_c, item_t = item
item_c = item_c
if k == 0 or k == last_index: # not do anything with the first and last item
item = list(item) # tuple => list
zoomouted.append([item_c, item_t])
else:
if item_t - ref_t <= resolution:
ts.append(util.dat2time(item_t))
cs.append(item_c)
else:
zoomouted.append([
np.average(cs),
datetime.datetime.fromtimestamp(np.average(ts))
])
ref_t = item_t
cs, ts = [item_c], [util.dat2time(item_t)] # reinitialize ts, cs
return zoomouted
def inte_coresec(xs, ys):
# inte: integrate core seconds
# xs: usually time along the x axis
# ys: core seconds along the y axis
# The math is just like calculating the area of tiny trapezoids, and then
# sum it up.
xs = [util.dat2time(i) for i in xs]
ref_x = xs[0]
ref_y = ys[0]
area = 0
for x, y in zip(xs[1:], ys[1:]):
area = area + (.5 * (y + ref_y) * (x - ref_x))
ref_x, ref_y = x, y
return area # core seconds
def inte_coresec(xs, ys):
# inte: integrate core seconds
# xs: usually time along the x axis
# ys: core seconds along the y axis
# The math is just like calculating the area of tiny trapezoids, and then
# sum it up.
xs = [util.dat2time(i) for i in xs]
ref_x = xs[0]
ref_y = ys[0]
area = 0
for x, y in zip(xs[1:], ys[1:]):
area = area + (.5 * (y + ref_y) * (x - ref_x))
ref_x, ref_y = x, y
return area # core seconds
