def fig_trans_load_vs_time(result_path, trans, env=None):
result = util.load_dict(result_path)
'''
temp = []
for i in result['base_load']:
temp.append(np.amin(np.array(env['transRating'])/np.array(result['base_load'][i]['trans_load'])))
print(np.amax(np.array(temp)))
'''
x = []
for key in result:
for subKey in result[key]:
x.append(subKey)
break
x = np.array(x)
x = np.sort(x)
fig, ax = plt.subplots()
ax.xaxis.set_major_locator(MultipleLocator(6))
ax.xaxis.set_major_formatter(FuncFormatter(format_func))
ax.xaxis.set_minor_locator(MultipleLocator(1))
#fig.add_subplot(221)
linewidth = 1.0
legend = []
y = 100 * np.ones(len(x))
legend.append('max')
plt.plot(x, y, '--', linewidth=linewidth)
for key in result:
if key == 'llf' or key == 'edf' or key == 'central' or key == 'base_load':
continue
y = []
legend.append(key)
for i in x:
y.append(result[key][i]['n_iter'])
y = np.array(y)
if key == 'base_load':
plt.plot(x, y, linewidth=linewidth, alpha=0.3)
else:
plt.plot(x, y, linewidth=linewidth)
plt.legend(legend)
plt.title('# of iterations required for 95% convergence')
plt.xlabel('Time')
plt.ylabel('# of iterations')
plt.xticks(rotation=30)
plt.show()
def fig_soc_vs_time(result_path, usr_type, algo, slot=60):
result = util.load_dict(result_path)
x = []
for key in result:
for subKey in result[key]:
x.append(subKey)
break
x = np.array(x)
x = np.sort(x)
legend = []
for key in result:
if key == algo:
y = [[], [], []]
temp = [[], [], []]
for i in x:
rd = result[key][i]['remaining_demand']
for j in range(0, len(rd)):
temp[usr_type[j]].append(rd[j])
temp_ = np.array(temp)
y[0].append(np.average(temp_[0]))
legend.append('usr_type ' + str(0))
y[1].append(np.average(temp_[1]))
legend.append('usr_type ' + str(1))
y[2].append(np.average(temp_[2]))
legend.append('usr_type ' + str(2))
y = np.array(y) / 3600.0
plt.plot(x, y[0])
plt.plot(x, y[1])
plt.plot(x, y[2])
plt.legend(legend)
plt.title('Remaining Demand: ' + algo)
plt.xlabel('Time Slots (1 slot = ' + str(slot) + ' sec)')
plt.ylabel('kWh')
plt.show()
from __future__ import print_function
from syllable import ESyl
from syllable import TSyl
from syllable import syllable_split
import utility
map_onset = utility.load_dict('dict_onset_v01')
map_nucleus = utility.load_dict('dict_nucleus_v01')
map_coda = utility.load_dict('dict_coda_v01')
map_rules = []
vowel = 'aeuio'
def mapping(words):
with open('rules') as fh:
for line in fh.readlines():
if len(line) == 0 or line[0] == '#':
continue
structs = line.split('\t')
assert len(structs[0].split(',')) == 3
on1, nu1, co1 = [part.strip() for part in structs[0].split(',')]
if len(structs) == 2:
assert len(structs[1].split(',')) == 3
on2, nu2, co2 = [
part.strip() for part in structs[1].split(',')
]
if on2 == '*':
assert on1 == '*'
for key, val in list(map_onset.items()):
map_rules.append(
[ESyl(key, nu1, co1), [TSyl(val, nu2, co2)]])
import numpy as np
import utility as util
import DSSStartup
import sys
from tqdm import tqdm
from algo import algo
from central_algo import central_algo
from decentral_algo import decentral_algo
from base_line_algo import base_line_algo
from base_load_algo import base_load_algo
simu_params = util.load_dict(sys.argv[1])
env = util.load_dict(simu_params['env_path'])
DSSObj = DSSStartup.dssstartup('master33Full.dss')
# Initial P and Q
PQ_dict = util.load_dict(simu_params['base_load_path'] + 'h' +
str(simu_params['start_hr']) + '.txt')
P = np.array(PQ_dict['P'])
Q = np.array(PQ_dict['Q'])
algo_list = {}
for key in simu_params['algo']:
if simu_params['algo'][key]['type'] == 'central_algo':
obj = central_algo(DSSObj,
env,
import numpy as np
import utility as util
file_name = 'static.txt'
result_path = 'result/'+file_name
env_path = 'env/'+file_name
result = util.load_dict(result_path)
env = util.load_dict(env_path)
def excursion_for_single_time_slot(trans_load, trans=[1], time=(1/6.0)):
rating = np.array(env['transRating'])
load = np.array(trans_load)
total = 0
for i in trans:
diff = 3*rating[3*i] - load[3*i] - load[3*i+1] - load[3*i+2]
if diff <= 0.0:
total -= diff
#print(total)
return total*time
algos = {'Central', 'GPA', 'SGPA', 'LLF', 'EDF'}
output = {}
for algo in algos:
total = 0.0
#print(algo)
for i in range(0,144):
#print(i)
'''
import sys
i = 1
print(str(i).zfill(5))
sys.exit()
'''
result_path = 'result/mix_500.txt'
base_load_path = 'base_load/10_min/'
env_path = 'env/mix_500.txt'
DSSObj = DSSStartup.dssstartup('master33Full.dss')
tol = 0.5
result = util.load_dict(result_path)
env = util.load_dict(env_path)
slot = 140
rho = 1000.0
#print(result['central'][slot]['x'])
connected = result['Central'][slot]['connected']
print('connected')
print(len(connected))
factor = 0.1
n_slot_per_hr = 6
h = slot//n_slot_per_hr
bl_scale = 1.3
PQ_dict = util.load_dict(base_load_path+'h'+str(h)+'.txt')
import numpy as np
#import matplotlib
import matplotlib.pyplot as plt
import utility as util
import sys
import plot
data = util.load_dict('data_conservative_risk_taker.txt')
algos = [key for key in data[0]]
w = 0.6
a = 3.0
u = 1.0
ind = []
x_ind = []
for i in range(0, len(algos)):
ind.append(a * i + 1)
x_ind.append(a * i + (u + 2) / 2)
ind = np.array(ind)
color = [['forestgreen', 'lightyellow'], ['firebrick', 'mistyrose']]
symbol = [['', '/'], ['\\', '-']]
label = [['Jain Index:All Conservative', 'EV %:All Conservative'],
['Jain Index:All Risk Taker', 'EV %:All Risk Taker']]
plt.rcParams.update({'font.size': 16})
fig = plt.figure()
ax = fig.add_subplot(111)
metric_index = {'jain': 0, 'soc': 1}
def fig_trans_load_subplot(result_path, trans_list, env):
result = util.load_dict(result_path)
x = []
for key in result:
for subKey in result[key]:
x.append(subKey)
break
x = np.array(x)
x = np.sort(x)
plt.rcParams.update({'font.size': 24})
fig, ax = plt.subplots(sharex=True, sharey=True)
#ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')
#ax.yaxis.set_ticks_position('left')
ax.yaxis.set_ticks([])
ax.xaxis.set_ticks([])
ax.set_xlabel('\nTime')
ax.set_ylabel('MVA\n')
#fig.add_subplot(221)
linewidth = 1.0
ii = 1
for trans in trans_list:
ax = fig.add_subplot(len(trans_list), 1, ii)
if trans == 0:
ax.xaxis.set_major_locator(MultipleLocator(24))
ax.xaxis.set_major_formatter(FuncFormatter(format_func))
ax.xaxis.set_minor_locator(MultipleLocator(12))
else:
ax.get_xaxis().set_visible(False)
legend = []
#legend.append('')
for key in result:
if key == 'SGPA' or key == 'GPA':
continue
y = []
legend.append(key)
for i in x:
y.append(result[key][i]['trans_load'][(3 * trans + 0)] +
result[key][i]['trans_load'][(3 * trans + 1)] +
result[key][i]['trans_load'][(3 * trans + 2)])
y = np.array(y) / 1000
if key == 'Base Load':
plt.plot(x, y, linewidth=linewidth, alpha=0.3)
else:
plt.plot(x, y, linewidth=linewidth)
if ii == 1:
#plt.legend()
#plt.legend(legend, loc='upper left')
plt.legend(legend)
'''
if trans==0:
plt.title('Substation Loading')
else:
plt.title('Transformer (#'+ str(trans)+')'+' Loading')
'''
y = 3 * env['transRating'][3 * trans] * np.ones(len(x)) / 1000
plt.plot(x, y, '--', linewidth=linewidth)
#plt.xticks(rotation=30)
#plt.yticks([])
ii += 1
plt.show()
def fig_compare(result_path, last_slot, env):
output = {}
result = util.load_dict(result_path)
#men_means, men_std = (20, 35, 30, 35, 27), (2, 3, 4, 1, 2)
#women_means, women_std = (25, 32, 34, 20, 25), (3, 5, 2, 3, 3)
fig, ax = plt.subplots()
for user_type in [0, 1]:
output[user_type] = {}
jain_means = []
jain_std = []
soc_means = []
soc_std = []
if user_type == -1:
battery = np.array(env['battery'])
else:
battery = np.array([
env['battery'][i] for i in range(0, env['evNumber'])
if env['evDriverType'][i] == user_type
])
#print(len(battery))
algo_name = []
for key in result:
if key == 'Base Load':
continue
output[user_type][key] = {}
algo_name.append(key)
temp = []
for subKey in result[key]:
#if key=='central':
#print(result[key][subKey]['x'])
if user_type == -1:
value = result[key][subKey]['x']
else:
c = result[key][subKey]['connected']
l = len(c)
value = [
result[key][subKey]['x'][i] for i in range(0, l)
if env['evDriverType'][c[i]] == user_type
]
'''
if 'w' in result[key][subKey]:
w = [result[key][subKey]['w'][i] for i in range(0,l) if env['evDriverType'][c[i]]==user_type]
else:
w = np.ones(len(value)).tolist()
'''
temp.append(util.jain_index(value))
temp = np.array(temp)
output[user_type][key]['jain'] = (np.average(temp), np.std(temp))
jain_means.append(np.average(temp))
jain_std.append(np.std(temp))
if user_type == -1:
remaining_demand = np.array(
result[key][last_slot]['remaining_demand'])
else:
remaining_demand = np.array([
result[key][last_slot]['remaining_demand'][i]
for i in range(0, env['evNumber'])
if env['evDriverType'][i] == user_type
])
#print(key)
#print(remaining_demand)
temp = (battery - remaining_demand) / battery
count = 0
#print(key)
#print(temp)
for i in range(len(temp)):
if temp[i] >= 0.9:
count += 1
output[user_type][key]['soc'] = (count / len(temp), 0.0)
soc_means.append(count / len(temp))
soc_std.append(0.0)
ind = np.arange(len(jain_means)) # the x locations for the groups
width = 0.3 # the width of the bars
if user_type == 0:
rec = ax.bar(ind - width / 2,
jain_means,
width,
yerr=jain_std,
label='Jain Index:Conservative',
hatch='/')
h_offset = autolabel(rec, ax)
rec = ax.bar(ind - width / 2,
soc_means,
width,
yerr=soc_std,
bottom=jain_means,
label='% of EV:Conservative')
autolabel(rec, ax, h_offset=h_offset)
else:
rec = ax.bar(ind + width / 2,
jain_means,
width,
yerr=jain_std,
label='Jain Index:Risk Taker',
hatch='*')
h_offset = autolabel(rec, ax)
rec = ax.bar(ind + width / 2,
soc_means,
width,
yerr=soc_std,
bottom=jain_means,
label='% of EV:Risk Taker')
autolabel(rec, ax, h_offset=h_offset)
#autolabel(rects1, ax, "left")
#autolabel(rects2, ax, "right")
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Values')
'''
if user_type==-1:
ax.set_title('Performances for all EVs')
elif user_type==0:
ax.set_title('Performances for Conservative EVs')
elif user_type==1:
ax.set_title('Performances for Risk-Taking EVs')
else:
ax.set_title('Performances for DishonestRisk-Taker EVs')
'''
ax.set_xticks(ind)
#ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
ax.set_xticklabels(algo_name)
ax.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=5,
fancybox=True,
shadow=True)
#fig.tight_layout()
#plt.show()
print(output)
util.save_dict('data_mix_500_2.txt', output)
x = np.array(x)
x = np.sort(x)
linewidth = 1.0
y = []
for key in x:
y.append(result['gamma'][key])
#plt.legend([])
plt.plot(x, y)
plt.title('95% Convergence')
plt.xlabel('gamma')
plt.ylabel('iter')
plt.show()
if __name__ == '__main__':
simu_params = util.load_dict('simu_params.txt')
#env = util.load_dict('env/large.txt')
env = util.load_dict(simu_params['env_path'])
#fig_soc_vs_time(simu_params['save_path'], (env['evDriverType']), algo='central')
#fig_trans_load_vs_time(simu_params['save_path'], trans=0, env=env)
fig_trans_load_subplot(simu_params['save_path'],
trans_list=[1, 0],
env=env)
#fig_compare(simu_params['save_path'], 143, env)
#fig_conv_ana('result/meta_large.txt')
return sum(1 for word in words if len(word) == 1)
def bi_segment(text: str, dic: dict) -> list:
f = forward_segment(text, dic)
b = backward_segment(text, dic)
if len(f) < len(b):
return f
elif len(f) > len(b):
return b
else:
if (count_single(f) < count_single(b)):
return f
else:
return b
if __name__ == "__main__":
dic = load_dict()
words = fully_segment("商品和服务", dic)
print(words)
words = forward_segment("研究生命的起源", dic)
print(words)
words = backward_segment("研究生命的起源", dic)
print(words)
words = bi_segment("当下雨天地面积水", dic)
print(words)
