def process_smbh_cluster(H0, WM, WV):
smbh_cluster_file = 'SMBH_cluster'
smbh_cluster = get_cluster_file(os.path.join(cluster_folder, smbh_cluster_file))
for i, line in enumerate(smbh_cluster):
sys.stdout.write(''.join(['\rConverting z to age in SMBH Cluster for #', str(i+1), ' of ',
str(len(smbh_cluster))]))
sys.stdout.flush()
z = float(line[0])
input_params= [z, H0, WM, WV]
age = CC.get_output_params(input_params)
line.insert(1, age)
print
smbh_cluster = np.array(smbh_cluster, dtype=object)
smbh_cluster = sorted(smbh_cluster, key=lambda l:l[2])
F = open(os.path.join(cluster_folder, 'smbh_cluster.pkl'), 'wb')
pickle.dump(smbh_cluster, F)
F.close()
print('Saved SMBH Cluster array to pickle')
print
print(smbh_cluster[0])
print(smbh_cluster[int(len(smbh_cluster)/4)])
print(smbh_cluster[int(len(smbh_cluster)/2)])
print(smbh_cluster[3*int(len(smbh_cluster)/4)])
print(smbh_cluster[len(smbh_cluster)-1])
return smbh_cluster
def process_galaxies_cluster(H0, WM, WV):
galaxies_cluster_file = 'galaxies_cluster'
galaxies_cluster = get_cluster_file(os.path.join(cluster_folder, galaxies_cluster_file))
galaxies_cluster_length = len(galaxies_cluster)
for i, line in enumerate(galaxies_cluster):
if len(line) >= 6:
line[5] = line[5:]
if len(line) >= 7:
line[6:] = []
sys.stdout.write(''.join(['\rConverting z to age in Galaxy Cluster for #', str(i+1), ' of ',
str(galaxies_cluster_length)]))
sys.stdout.flush()
z = float(line[0])
input_params= [z, H0, WM, WV]
age = CC.get_output_params(input_params)
line.insert(1, age)
print
#galaxies_cluster = np.array([np.array(line, dtype=object) for line in galaxies_cluster], dtype=object)
galaxies_cluster = np.array(galaxies_cluster, dtype=object)
galaxies_cluster = sorted(galaxies_cluster, key=lambda l:l[2])
# print(galaxies_cluster)
F = open(os.path.join(cluster_folder, 'galaxies_cluster.pkl'), 'wb')
pickle.dump(galaxies_cluster, F)
F.close()
print('Saved Galaxies Cluster array to pickle')
return galaxies_cluster
def highlight_each_cc_driver(file, verbose=False, min_size=-1):
s = time.perf_counter()
cc_im = CC.CC_Image(file, verbose)
if (min_size == -1):
min_size = .0001 * cc_im.original.size / 3
print("No minimum component size given, defaulting to .01%: " +
str(min_size) + " pixels")
gray_img = cc_im.rgb_to_gray(deepcopy(cc_im.original))
binary_img = cc_im.gray_to_binary(gray_img)
labels = cc_im.remove_small_ccs(cc_im.two_pass_cc(binary_img), min_size)
colored_img, color_dict = cc_im.color_all_ccs(labels)
cc_count = cc_im.number_of_ccs(labels)
unique_labels = np.unique(labels)
ul_len = len(unique_labels)
t = time.perf_counter()
components = []
for i, x in enumerate(
np.delete(unique_labels, np.argwhere(unique_labels == 0))):
img = cc_im.highlight_one_cc(labels, colored_img, x)
components.append(img)
global img_counter
fig = plt.figure()
fig.canvas.set_window_title("Component # " + str(img_counter))
fig.canvas.mpl_connect(
'key_press_event', lambda event: change_component_with_key(
event.key, fig, components, colored_img, cc_im.original))
print("Highlight all CC's:", '%.6f' % (time.perf_counter() - t))
plt.subplot(1, 3, 1)
plt.imshow(cc_im.original)
plt.axis('off')
plt.subplot(1, 3, 2)
plt.imshow(colored_img)
plt.axis('off')
plt.subplot(1, 3, 3)
plt.imshow(components[img_counter])
plt.axis('off')
#img_counter += 1
print("Full runtime:", '%.6f' % (time.perf_counter() - s))
print("Found " + str(cc_count) + " connected components")
plt.show()
return
def __init__(self):
self.c_s_c = 0 # current slave count
self.slave_count = 1
self.start_time = 0.0
self.f_complexity_list = [] # file complexity list
file_list, path = CC.git_expand(
"https://github.com/Vkanishka/ChatApp.git")
self.path = path
self.file_list = file_list
self.total_file = len(self.file_list)
print("number count of commits: {}".format(self.total_file))
def Classes(self, dept, teacher, name, number, week, sect):
dept = CC.S2T(dept)
teacher = CC.S2T(teacher)
name = CC.S2T(name)
result = []
sql = "SELECT * FROM `classes` WHERE `Department` LIKE '%" + str(dept) + "%' AND `Teacher` LIKE '%" + str(teacher) + "%' AND `Name` LIKE '%" + str(name) + "%' AND `Number` LIKE '%" + str(number) + "%';"
for r in self.Query(sql):
c = ClassDetail(r[1:])
t = c.Table
if week != '' and sect != '' and t[int(week) - 1].find(sect) >= 0:
result.append(c)
elif week == '' and sect != '':
for i in range(7):
if t[i].find(sect) >= 0:
result.append(c)
elif week != '' and sect == '':
if t[int(week) - 1] != '':
result.append(c)
elif week == '' and sect == '':
result.append(c)
return result
def Crawl(self, obj=False):
bs = self.FixToBS()
# 找寻其所有页数
final_tr = bs.find_all('tr')[-1]
pattern = RE.compile(r'第 ([0-9]+) \/ ([0-9]+) 頁')
match = pattern.match(final_tr.get_text())
self.Pages = int(match[2])
# 开始把找到的列物件化
result = []
for tr in bs.find_all('tr')[3:-2]: # 去头去尾
tmp = []
idx = 0
for td in tr.find_all('td'):
# 学校代码问题，会超出 25
if idx < 25:
tmp_text = td.get_text().strip().replace(' ', '-')
if idx == 7:
# 课名只取中文
name = td.find('a').get_text().strip()
tmp.append(name)
tmp.append(CC.T2S(name)) # 简体字课名
tmp.append(td.find('a')['href']) # 课纲网址
elif idx == 3 or idx == 15:
# 简体字系所、老师
tmp.append(tmp_text)
tmp.append(CC.T2S(tmp_text))
else:
tmp.append(tmp_text)
else:
break
idx += 1
if obj:
result.append(ClassDetail(tmp))
else:
result.append(tmp)
return result
def run():
# z_s = [4, 3.1, 2.8, 2.5, 2.2, 2.0, 1.9, 1.75, 1.6, 1.5, 1.35, 1.3, 1.25, 1.2, 1.15, 1.1, 1.05, 1.0, 0.95, 0.9, 0.85,
# 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.25, 0.2, 0.15, 0.1, 0.05, 0.0]
z_s = np.linspace(4., 0., num=1000)
print z_s
age = []
for z in z_s:
input_params= [z, H0, WM, WV]
age.append(CC.get_output_params(input_params))
print age
sigma = np.ones(len(age))
sigma[[0, -1]] = 0.02
popt, pcov = scipy.optimize.curve_fit(func, age, z_s, sigma=sigma)
print popt
z_est = np.array([func(x, *tuple(popt)) for x in age])
# print zip(age, z_est)
plt.figure(1)
plt.plot(age, z_s, label='t-to-z func')
plt.plot(age, z_est, label='curve fit')
locs, labels = plt.xticks()
plt.setp(labels, rotation=45)
# plt.xticks(np.arange(1.5, 14., 0.5))
plt.yticks(np.arange(0., 4.5, 0.5))
plt.legend()
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.xlabel('Age (Gyr)')
plt.ylabel('Z')
plt.figure(2)
diff = [(zest-zs)/zs for (zest, zs) in zip(z_est, z_s)]
plt.plot(age, diff)
plt.xlabel('Age (Gyr)')
plt.ylabel('(z_est-z_s)/z_s')
locs, labels = plt.xticks()
plt.setp(labels, rotation=45)
plt.xticks(np.arange(1.5, 14., 0.5))
plt.yticks(np.arange(-0.5, 9., 0.5))
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.title('Difference ratio between curve fit and z_function')
plt.show()
def runkMeans(X,initial_centroids,max_iters,plot_progress=None):
if plot_progress == None :
plot_progress=False
#if plot_progress:
shape=X.shape
m=shape[0]
K=initial_centroids.shape[0]
centroids=initial_centroids
previous_centroids=centroids
idx=np.zeros((m,1))
for i in range(max_iters):
print 'K-Means iteration： %d %d' % (i,max_iters)
idx=FCC.findClosestCentroids(X,centroids)
if plot_progress:
PPKM.plotProgresskMeans(X,centroids,previous_centroids,idx,K,i)
previous_centroids=centroids
centroids= CC.computeCentroids(X,idx,K)
return (centroids,idx)
def rgb_to_ccs_driver(file, verbose=False, min_size=-1):
s = time.perf_counter()
cc_im = CC.CC_Image(file, verbose)
if (min_size == -1):
min_size = .0001 * cc_im.original.size / 3
print("No minimum component size given, defaulting to .01%: " +
'%.6f' % (min_size) + " pixels")
gray_img = cc_im.rgb_to_gray(deepcopy(cc_im.original))
binary_img = cc_im.gray_to_binary(gray_img)
labels = cc_im.remove_small_ccs(cc_im.two_pass_cc(binary_img), min_size)
colored_img, color_dict = cc_im.color_all_ccs(labels)
plt.subplot(2, 2, 1)
plt.imshow(cc_im.original)
plt.axis('off')
plt.subplot(2, 2, 2)
plt.imshow(gray_img, cmap="gray")
plt.axis('off')
plt.subplot(2, 2, 3)
plt.imshow(binary_img, cmap="gray")
plt.axis('off')
plt.subplot(2, 2, 4)
plt.imshow(colored_img)
plt.axis('off')
print("Full runtime:", '%.6f' % (time.perf_counter() - s))
print("Found " + str(cc_im.number_of_ccs(labels)) +
" connected components")
plt.show()
return
def main(argv=None):
if argv == None:
argv = sys.argv
# this is the global time, it goes up every tick, it is global
global global_time
global_time = 0
sending_link = Link(8, True)
recving_link = Link(8, False)
# TODO P4 accept neural network configuration and network topology from
# command line or input file
hidden_neurons = 7
max_send_queue = 5
logfilename = "ssim.trc"
oracle = Oracle()
oracle.reg_link(sending_link)
oracle.reg_link(recving_link)
for i in range(1, 5):
snd_agt = sending_agent(sending_link,
CC.NCC(hidden_neurons, max_send_queue), i)
rcv_agt = recving_agent(recving_link, 0 - i)
sending_link.register(i, snd_agt)
recving_link.register(0 - i, rcv_agt)
oracle.reg_agt(snd_agt)
try:
logout = open(logfilename, 'w')
oracle.begin_log(logout)
except IOError as e:
print "Failed to open log."
net = Master_link(sending_link, recving_link)
net.tock()
oracle.flush_logs()
net.tock()
oracle.flush_logs()
net.tock()
oracle.flush_logs()
logout.close()
print 'One: =============Find Closest Centroids...'
data=sio.loadmat('ex7data2')
X=data['X']
K=3
initial_centroids=np.array([[3,3],[6,2],[8,5]])
idx=FCC.findClosestCentroids(X,initial_centroids)
print 'Closest Centroids for the first 3 examples: '
print idx[0:3]
print '(the closest centroids should be 1, 3, 2 respectively)'
#Part Two: Compute Means
print 'Two: =============Compute Means ...'
centroids=CC.computeCentroids(X,idx,K)
print 'Centroids computed after initial finding of closest centroids: '
print centroids
print '( the centroids should be [ [ 2.428301 3.157924 ] [ 5.813503 2.633656 ] [ 7.119387 3.616684 ]]'
#Part Three: K-Means Clustering
print 'Three: ================== K-Means Clustering ...'
max_iters=10
res=RKM.runkMeans(X,initial_centroids,max_iters,True)
plt.show()
print 'K-Means Done...'
#Part Four: K-Means Clustering on Pixels
print 'Running K-Means clustering on pixels from an image '
img=cv2.imread('bird_small.png')
from time import time
from GeneralisedWick import *
import CC, texify, pickle
fockTensor = Tensor("f", ['g'], ['g'])
h2Tensor = Tensor("v", ['g', 'g'], ['g', 'g'])
fockTensor.getAllDiagramsGeneral()
h2Tensor.getAllDiagramsGeneral()
t1Tensor = Tensor("{t_{1}}", ['p'], ['h'])
t2Tensor = Tensor("{t_{2}}", ['p', 'p'], ['h', 'h'])
normalOrderedHamiltonian = sum(
fockTensor.diagrams) + (1. / 2.) * sum(h2Tensor.diagrams)
BCH = CC.BCHSimilarityTransform(normalOrderedHamiltonian,
t1Tensor + 0.5 * t2Tensor, 4)
t0 = time()
energyEquation = CC.getEnergyEquation(BCH)
t1 = time()
print("Time to find energy equation:", t1 - t0)
print("number of terms:", len(energyEquation.summandList))
singlesAmplitudeEquation = CC.getAmplitudeEquation(BCH, 1)
t2 = time()
print("Time to find singles amplitude equation:", t2 - t1)
print("number of terms:", len(singlesAmplitudeEquation.summandList))
doublesAmplitudeEquation = CC.getAmplitudeEquation(BCH, 2)
#for summand in doublesAmplitudeEquation.summandList:
# summand.prefactor *= (1. / 2.)
t3 = time()
print("Time to find doubles amplitude equation:", t3 - t2)
print("\n")
print("CCSD")
trueCCSD = cc.CCSD(mf)
#old_update_amps = trueCCD.update_amps
#def update_amps(t1, t2, eris):
# t1, t2 = old_update_amps(t1, t2, eris)
# print(t1)
# return (np.zeros_like(t1[0]), np.zeros_like(t1[1])), t2
#trueCCD.update_amps = update_amps
print(trueCCSD.kernel())
t7 = time()
print("CCSD time:", t7 - t6)
singlesResidual = Tensor("R", ['p'], ['h'])
singlesResidual.array = contractTensorSum(singlesAmplitudeEquation)
doublesResidual = Tensor("R", ['p', 'p'], ['h', 'h'])
doublesResidual.array = contractTensorSum(doublesAmplitudeEquation)
#print(residual.array)
#print(h2Tensor.diagrams[12].array)
CC.iterateDoublesAmplitudes(t2Tensor, doublesResidual, fockTensor.array)
t5 = time()
print("Time for MP2 calculation:", t5 - t7)
print(contractTensorSum(energyEquation))
print(t2Tensor.array)
CC.convergeCCSDAmplitudes(t1Tensor, t2Tensor, energyEquation,
singlesAmplitudeEquation, doublesAmplitudeEquation,
fockTensor)
t2 = time()
print("Time for CCSD calculation:", t2 - t5)
game = Game(
first_screen='Wybrales KPNLS .....',
possible_choices='KPNLS',
choice_screen=
'Podaj swoj wybor: \nK - kamien \nP - papier \nN - nozyczki\n L - lizard\n S - spock',
remis=['KK', 'PP', 'NN', 'LL', 'SS'],
win=['PK', 'NP', 'KN', 'SK', 'LP', 'SN', 'NL', 'KL', 'PS', 'LS'],
loss=['KP', 'PN', 'NK', 'KS', 'PL', 'NS', 'LN', 'LK', 'SP', 'SL'])
game.play()
if data == []:
print('KONIEC GRY\n')
else:
import CC
a = CC.win_loss_sequence(data, 'W')
b = len(data)
c = CC.win_loss_sequence(data, 'P')
print('wyniki rozgrywek: ', data)
print('ilosc WYGRANYCH uzytkownika: ', data.count('W'))
print('ilosc PRZEGRANYCH uzytkownika: ', data.count('P'))
# jesli W = 0 i P = 0 => ZeroDivisionError (np. dla data = ['remis', 'remis'] dlatego:
if (data.count('W') + data.count('P')) > 0:
print('ilosc WYGRANYCH uzytkownika w %: ',
data.count('W') / (data.count('W') + data.count('P')) * 100, '%')
print('ilosc PRZEGRANYCH uzytkownika w %: ',
data.count('P') / (data.count('W') + data.count('P')) * 100, '%')
else:
print(
print("\n")
print("CCD")
trueCCD = cc.CCSD(mf)
old_update_amps = trueCCD.update_amps
def update_amps(t1, t2, eris):
t1, t2 = old_update_amps(t1, t2, eris)
# print(t1)
return (np.zeros_like(t1[0]), np.zeros_like(t1[1])), t2
trueCCD.update_amps = update_amps
print(trueCCD.kernel())
t7 = time()
print("CCD time:", t7 - t6)
residual = Tensor("R", ['p', 'p'], ['h', 'h'])
residual.array = contractTensorSum(doublesAmplitudeEquation)
print(residual.array)
print(h2Tensor.diagrams[12].array)
CC.iterateDoublesAmplitudes(t2Tensor, residual, fockTensor.array)
t5 = time()
print("Time for MP2 calculation:", t5 - t7)
print(contractTensorSum(energyEquation))
print(t2Tensor.array)
CC.convergeDoublesAmplitudes(t2Tensor, energyEquation,
doublesAmplitudeEquation, fockTensor)
t2 = time()
print("Time for CCD calculation:", t2 - t5)
first_screen='Wybrales KPNLS .....',
possible_choices='KPNLS',
choice_screen = 'Podaj swoj wybor: \nK - kamien \nP - papier \nN - nozyczki\n L - lizard\n S - spock',
remis = ['KK', 'PP', 'NN', 'LL', 'SS'],
win = ['PK', 'NP', 'KN', 'SK', 'LP', 'SN', 'NL', 'KL', 'PS', 'LS'],
loss = ['KP', 'PN', 'NK', 'KS', 'PL', 'NS', 'LN', 'LK', 'SP', 'SL']
)
game.play()
if data == []:
print ('KONIEC GRY\n')
else:
import CC
a = CC.win_loss_sequence(data, 'W')
b = len(data)
c = CC.win_loss_sequence(data, 'P')
print ('wyniki rozgrywek: ', data)
print ('ilosc WYGRANYCH uzytkownika: ', data.count('W'))
print ('ilosc PRZEGRANYCH uzytkownika: ', data.count('P'))
# jesli W = 0 i P = 0 => ZeroDivisionError (np. dla data = ['remis', 'remis'] dlatego:
if (data.count('W') + data.count('P')) > 0:
print ('ilosc WYGRANYCH uzytkownika w %: ', data.count('W')/(data.count('W') + data.count('P'))*100, '%')
print ('ilosc PRZEGRANYCH uzytkownika w %: ', data.count('P')/(data.count('W') + data.count('P'))*100, '%')
else:
print ('ilosc WYGRANYCH uzytkownika w %: 0\nilosc PRZEGRANYCH uzytkownika w %: 0')
print ('najdluzsza sekwencja WYGRANYCH uzytkownika:', a)
H 0.000000, 0.000000, 2.6437904102
''',
basis='sto-3g',
#basis = 'cc-pVDZ',
symmetry='C2v',
)
##---------------------------------------------------------##
mf = scf.RHF(mol).run()
#mp2_res = MP2.MP2(mf)
#mp2_res.nfo = 1
#mp2_res.run()
cc_res = CC.state(mf)
cc_res.variant = 'CCSD'
if (cc_res.variant == 'ICCSD'):
cc_res.no_act = 1
cc_res.nv_act = 1
cc_res.maxsub = 7
#cc_res.conv = 1e-7
cc_res.maxiter = 50
cc_res.energy.run()
cc_res.maxiter = 30
cc_res.conv = 1e-6
cc_res.maxsub = 40
def createCC(self, last4, custId, name, ccNum, ccExp, ccv):
self.userCCDic[last4] = CC(last4, custId, name, ccNum, ccExp, ccv)
from time import time
from GeneralisedWick import *
import CC, texify
from pyscf import cc, mp
fockTensor = Tensor("f", ['g'], ['g'])
h2Tensor = Tensor("v", ['g', 'g'], ['g', 'g'])
fockTensor.getAllDiagramsGeneral()
h2Tensor.getAllDiagramsGeneral()
t1Tensor = Tensor("{t_{1}}", ['p'], ['h'])
t2Tensor = Tensor("{t_{2}}", ['p', 'p'], ['h', 'h'])
normalOrderedHamiltonian = sum(fockTensor.diagrams) + (1. / 2.) * sum(h2Tensor.diagrams)
BCH = CC.BCHSimilarityTransform(normalOrderedHamiltonian, t1Tensor + 0.25 * t2Tensor, 4)
t0 = time()
energyEquation = CC.getEnergyEquation(BCH)
t1 = time()
print("Time to find energy equation:", t1 - t0)
print("number of terms:", len(energyEquation.summandList))
singlesAmplitudeEquation = CC.getAmplitudeEquation(BCH, 1)
t2 = time()
print("Time to find singles amplitude equation:", t2 - t1)
print("number of terms:", len(singlesAmplitudeEquation.summandList))
doublesAmplitudeEquation = CC.getAmplitudeEquation(BCH, 2)
t3 = time()
print("Time to find doubles amplitude equation:", t3 - t2)
print("number of terms:", len(doublesAmplitudeEquation.summandList))
# links.npz can be downloaded from https://s3.amazonaws.com/Harvard-CS205/wikipedia/links.npz
# It is an (130148107, 2) array of np.uint32_t, generated by preprocess_to_npz.py
# Self-links have been removed.
# link names can be downloaded from https://s3.amazonaws.com/Harvard-CS205/wikipedia/titles-sorted.txt
# Note that the code below converts link indices from 1-indexed to 0-indexed.
if __name__ == '__main__':
with timer.Timer() as t:
print("Reading links")
links = np.load('links.npz')['links'] - 1
print(" {} links in graph".format(links.shape[0]))
print("Reading names")
names = [l.strip() for l in open('titles-sorted.txt')]
print(" {} nodes in graph".format(len(names)))
print("Loading: {} seconds\n".format(t.interval))
num_nodes = len(names)
total_time = 0
for i in range(10):
links_copy = links.copy() # just in case
with timer.Timer() as t:
labels = CC.connected_components(num_nodes, links_copy)
unique_labels = np.unique(labels)
print("iter: {}: {} components".format(i + 1, unique_labels.size))
total_time += t.interval
print("Total time: {} seconds".format(total_time))
