def run(sc_file):
print()
bd = os.path.dirname(sc_file)
sc = scenario_factory.Scenario()
sc.load_JSON(sc_file)
print(sc.title)
plot_samples_carpet(sc, bd)
plt.show()
def run(sc_file):
print()
bd = os.path.dirname(sc_file)
sc = scenario_factory.Scenario()
sc.load_JSON(sc_file)
print(sc.title)
sample_data = np.load(p(bd, sc.run_pre_samplesfile))[:, :96]
unctrl = np.load(p(bd, sc.run_unctrl_datafile))[:, 0, :96]
fig, ax = plt.subplots(len(sample_data))
if len(sample_data) == 1:
ax = [ax]
for i, samples in enumerate(sample_data):
t = np.arange(samples.shape[-1])
for s in samples:
ax[i].plot(t, s)
ax[i].plot(np.arange(unctrl[i].shape[-1]), unctrl[i], 'k', lw=1.0)
plt.show()
import sys
import os
import datetime
import pickle
import numpy as np
import scenario_factory
import simulator
sc_file = sys.argv[1]
sc = scenario_factory.Scenario()
sc.load_JSON(sc_file)
sc.run_pre_ts = datetime.datetime.now()
d = os.path.dirname(sc_file)
sam_dfn = str(os.path.join(d, '.'.join((str(sc.seed), 'samples', 'npz'))))
if os.path.exists(sam_dfn):
raise RuntimeError('File already exists: %s' % sam_dfn)
sta_dfn = str(os.path.join(d, '.'.join((str(sc.seed), 'states', 'pickle'))))
if os.path.exists(sta_dfn):
raise RuntimeError('File already exists: %s' % sta_dfn)
ssd_dfn = str(
os.path.join(d, '.'.join((str(sc.seed), 'samples_sim_data', 'npz'))))
if os.path.exists(ssd_dfn):
raise RuntimeError('File already exists: %s' % ssd_dfn)
sample_data, states_data, sample_sim_data = simulator.run_pre(sc)
np.savez(sam_dfn, **sample_data)
with open(sta_dfn, 'wb') as outfile:
def stats(fn):
sc_file = fn
bd = os.path.dirname(sc_file)
sc = scenario_factory.Scenario()
sc.load_JSON(sc_file)
print(sc.title)
unctrl = load(p(bd, sc.run_unctrl_datafile))
block = load(p(bd, sc.run_ctrl_datafile))
post = load(p(bd, sc.run_post_datafile))
sched = load(p(bd, sc.sched_file))
ctrl = np.zeros(unctrl.shape)
idx = 0
for l in (block, post):
ctrl[:,:,idx:idx + l.shape[-1]] = l
idx += l.shape[-1]
if sched.shape[-1] == unctrl.shape[-1] / 15:
print('Extending schedules shape by factor 15')
sched = sched.repeat(15, axis=1)
t_start, b_start, b_end = sc.t_start, sc.t_block_start, sc.t_block_end
div = 1
if (b_end - t_start).total_seconds() / 60 == sched.shape[-1] * 15:
div = 15
elif (b_end - t_start).total_seconds() / 60 == sched.shape[-1] * 60:
div = 60
b_s = (b_start - sc.t_start).total_seconds() / 60 / div
b_e = (b_end - sc.t_start).total_seconds() / 60 / div
ctrl_sched = np.zeros((unctrl.shape[0], unctrl.shape[-1]))
ctrl_sched = np.ma.array(ctrl_sched)
ctrl_sched[:,:b_s] = np.ma.masked
ctrl_sched[:,b_s:b_e] = sched[:,b_s:b_e]
ctrl_sched[:,b_e:] = np.ma.masked
# plot_each_device(sc, unctrl, ctrl, sched)
minutes = (sc.t_end - sc.t_start).total_seconds() / 60
assert unctrl.shape[-1] == ctrl.shape[-1] == ctrl_sched.shape[-1]
shape = unctrl.shape[-1]
if minutes == shape:
print('data is 1-minute resolution, will be resampled by 15')
res = 15
elif minutes == shape * 15:
print('data is 15-minute resolution, all fine')
res = 1
else:
raise RuntimeError('unsupported data resolution: %.2f' % (minutes / shape))
unctrl = resample(unctrl, res)
ctrl = resample(ctrl, res)
ctrl_sched = resample(ctrl_sched, res)
# code above is from analyze
###########################################################################
# code below calculates stats
t_day_start = sc.t_block_start - timedelta(hours=sc.t_block_start.hour,
minutes=sc.t_block_start.minute)
skip = (t_day_start - sc.t_start).total_seconds() / 60 / 15
i_block_start = (sc.t_block_start - t_day_start).total_seconds() / 60 / 15
i_block_end = (sc.t_block_end - t_day_start).total_seconds() / 60 / 15
P_el_unctrl = unctrl[:,0,skip + i_block_start:skip + i_block_end].sum(0)
P_el_ctrl = ctrl[:,0,skip + i_block_start:skip + i_block_end].sum(0)
P_el_sched = ctrl_sched[:,skip + i_block_start:skip + i_block_end].sum(0)
slp = _read_slp(sc, bd)[skip + i_block_start:skip + i_block_end]
# Stats
pairs = [
# (P_el_sched, P_el_ctrl, 'P_el_sched', 'P_el_ctrl'),
(P_el_sched, P_el_unctrl, 'P_el_sched', 'P_el_unctrl'),
(P_el_unctrl, P_el_ctrl, 'P_el_unctrl', 'P_el_ctrl'),
]
st = [sc.title]
for target, data, tname, dname in pairs:
diff = obj(target, data)
perf = max(0, 1 - _f(target, data))
perf_abs = perf * 100.0
# if perf_abs > 100.0:
# perf_abs = 100 - min(100, max(0, perf_abs - 100))
print('obj(%s, %s) = %.2f kW (%.2f %%)' % (tname, dname, diff, perf_abs))
st.append(perf_abs)
# # SLP
# diff_ctrl = (P_el_ctrl - P_el_unctrl) / 1000.0
# slp_ctrl = slp + diff_ctrl
# slp_range = abs(slp.max() - slp.min())
# slp_range_ctrl = abs(slp_ctrl.max() - slp_ctrl.min())
# reduction = (1 - norm(0, slp_range, slp_range_ctrl)) * 100
# print('slp range = %.2f kW' % slp_range)
# print('slp range (ctrl) = %.2f kW' % slp_range_ctrl)
# print('reduction = %.2f %%' % reduction)
# st.append(reduction)
# SLP (only schedule)
diff_sched = (P_el_sched - P_el_unctrl) / 1000.0
slp_sched = slp + diff_sched
slp_range = abs(slp.max() - slp.min())
slp_range_sched = abs(slp_sched.max() - slp_sched.min())
reduction = (1 - norm(0, slp_range, slp_range_sched)) * 100
print('slp range = %.2f kW' % slp_range)
print('slp range (sched) = %.2f kW' % slp_range_sched)
print('reduction = %.2f %%' % reduction)
st.append(reduction)
print()
return st
def run(sc_file):
print()
bd = os.path.dirname(sc_file)
sc = scenario_factory.Scenario()
sc.load_JSON(sc_file)
print(sc.title)
# plot_samples(sc, bd)
# plt.show()
unctrl = np.load(p(bd, sc.run_unctrl_datafile))
pre = np.load(p(bd, sc.run_pre_datafile))
block = np.load(p(bd, sc.run_ctrl_datafile))
post = np.load(p(bd, sc.run_post_datafile))
sched = np.load(p(bd, sc.sched_file))
ctrl = np.zeros(unctrl.shape)
idx = 0
for l in (pre, block, post):
ctrl[:, :, idx:idx + l.shape[-1]] = l
idx += l.shape[-1]
if sched.shape[-1] == unctrl.shape[-1] / 15:
print('Extending schedules shape by factor 15')
sched = sched.repeat(15, axis=1)
ctrl_sched = np.zeros((unctrl.shape[0], unctrl.shape[-1]))
ctrl_sched = np.ma.array(ctrl_sched)
ctrl_sched[:, :pre.shape[-1]] = np.ma.masked
ctrl_sched[:, pre.shape[-1]:pre.shape[-1] + sched.shape[-1]] = sched
ctrl_sched[:, pre.shape[-1] + sched.shape[-1]:] = np.ma.masked
# plot_each_device(sc, unctrl, ctrl, sched)
minutes = (sc.t_end - sc.t_start).total_seconds() / 60
assert unctrl.shape[-1] == ctrl.shape[-1] == ctrl_sched.shape[-1]
shape = unctrl.shape[-1]
if hasattr(sc, 'slp_file'):
if minutes == shape:
print('data is 1-minute resolution, will be resampled by 15')
res = 15
elif minutes == shape * 15:
print('data is 15-minute resolution, all fine')
res = 1
else:
raise RuntimeError('unsupported data resolution: %.2f' %
(minutes / shape))
unctrl = resample(unctrl, res)
ctrl = resample(ctrl, res)
ctrl_sched = resample(ctrl_sched, res)
fig = plot_aggregated_SLP(sc, bd, unctrl, ctrl, ctrl_sched, res=15)
else:
if minutes == shape:
print('data is 1-minute resolution, will be resampled by 60')
res = 60
elif minutes == shape * 15:
print('data is 15-minute resolution, will be resampled by 4')
res = 4
elif minutes == shape * 60:
print('data is 60-minute resolution, all fine')
res = 1
else:
raise RuntimeError('unsupported data resolution: %.2f' %
(minutes / shape))
unctrl = resample(unctrl, res)
ctrl = resample(ctrl, res)
ctrl_sched = resample(ctrl_sched, res)
fig = plot_aggregated(sc, bd, unctrl, ctrl, ctrl_sched, res=60)
# fig.savefig(p(bd, sc.title) + '.pdf')
# fig.savefig(p(bd, sc.title) + '.png', dpi=300)
plt.show()
def stats(fn):
sc_file = fn
bd = os.path.dirname(sc_file)
sc = scenario_factory.Scenario()
sc.load_JSON(sc_file)
print(sc.title)
unctrl = load(p(bd, sc.run_unctrl_datafile))
block = load(p(bd, sc.run_ctrl_datafile))
post = load(p(bd, sc.run_post_datafile))
sched = load(p(bd, sc.sched_file))
ctrl = np.zeros(unctrl.shape)
idx = 0
for l in (block, post):
ctrl[:, :, idx:idx + l.shape[-1]] = l
idx += l.shape[-1]
if sched.shape[-1] == unctrl.shape[-1] / 15:
print('Extending schedules shape by factor 15')
sched = sched.repeat(15, axis=1)
t_start, b_start, b_end = sc.t_start, sc.t_block_start, sc.t_block_end
div = 1
if (b_end - t_start).total_seconds() / 60 == sched.shape[-1] * 15:
div = 15
elif (b_end - t_start).total_seconds() / 60 == sched.shape[-1] * 60:
div = 60
b_s = (b_start - sc.t_start).total_seconds() / 60 / div
b_e = (b_end - sc.t_start).total_seconds() / 60 / div
ctrl_sched = np.zeros((unctrl.shape[0], unctrl.shape[-1]))
ctrl_sched = np.ma.array(ctrl_sched)
ctrl_sched[:, :b_s] = np.ma.masked
ctrl_sched[:, b_s:b_e] = sched[:, b_s:b_e]
ctrl_sched[:, b_e:] = np.ma.masked
# plot_each_device(sc, unctrl, ctrl, sched)
minutes = (sc.t_end - sc.t_start).total_seconds() / 60
assert unctrl.shape[-1] == ctrl.shape[-1] == ctrl_sched.shape[-1]
shape = unctrl.shape[-1]
if minutes == shape:
print('data is 1-minute resolution, will be resampled by 60')
res = 60
elif minutes == shape * 15:
print('data is 15-minute resolution, will be resampled by 4')
res = 4
elif minutes == shape * 60:
print('data is 60-minute resolution, all fine')
res = 1
else:
raise RuntimeError('unsupported data resolution: %.2f' %
(minutes / shape))
unctrl = resample(unctrl, res)
ctrl = resample(ctrl, res)
ctrl_sched = resample(ctrl_sched, res)
# code above is from analyze
###########################################################################
# code below calculates stats
print('mean load: %.2f kW' % (unctrl[:, 0, :].sum(0).mean() / 1000.0))
t_day_start = sc.t_block_start - timedelta(hours=sc.t_block_start.hour,
minutes=sc.t_block_start.minute)
skip = (t_day_start - sc.t_start).total_seconds() / 60 / 60
i_block_start = (sc.t_block_start - t_day_start).total_seconds() / 60 / 60
i_block_end = (sc.t_block_end - t_day_start).total_seconds() / 60 / 60
P_el_unctrl = unctrl[:, 0, skip:].sum(0)
P_el_ctrl = ctrl[:, 0, skip:].sum(0)
P_el_sched = ctrl_sched[:, skip:].sum(0)
T_storage_ctrl = ctrl[:, 2, skip:]
# Stats
target = np.ma.zeros((minutes / 60 - skip, ))
target[:i_block_start] = np.ma.masked
target[i_block_start:i_block_end] = np.array(sc.block)
target[i_block_end:] = np.ma.masked
# print('target = %s' % target)
pairs = [
(target, P_el_sched, 'target', 'P_el_sched'),
(target, P_el_ctrl, 'target', 'P_el_ctrl'),
(target, P_el_unctrl, 'target', 'P_el_unctrl'),
(P_el_sched, P_el_ctrl, 'P_el_sched', 'P_el_ctrl'),
(P_el_sched, P_el_unctrl, 'P_el_sched', 'P_el_unctrl'),
(P_el_unctrl, P_el_ctrl, 'P_el_unctrl', 'P_el_ctrl'),
]
mask = target.mask
st = [sc.title]
for target, data, tname, dname in pairs:
target = np.ma.array(target, mask=mask)
data = np.ma.array(data, mask=mask)
diff = obj(target, data)
perf = max(0, 1 - _f(target, data))
perf_abs = perf * 100.0
# if perf_abs > 100.0:
# perf_abs = 100 - min(100, max(0, perf_abs - 100))
print('obj(%s, %s) = %.2f kW (%.2f %%)' %
(tname, dname, diff / 1000.0, perf_abs))
st.append(perf_abs)
# Synchronism
syncs = []
pairs = [
(i_block_start, 'block_start'),
(i_block_end, 'block_end'),
(23, 'day_end'),
(47, 'sim_end'),
]
for timestamp, name in pairs:
s = sync(T_storage_ctrl[:, timestamp] - 273) * 100.0
syncs.append(s)
print('sync(%s) = %.2f' % (name, s))
print()
return st, syncs
