def search(img_path):
cf = CaculateColorVector.get_color_feature(img_path)
cfs = File_Operation.read_list_from_file('/Users/ligang/Documents/cfs.txt')
distances = []
for cf_tuple in cfs:
d = Distance.distance(cf, cf_tuple[1])
distances.append((cf_tuple[0], d))
top_distances = heapq.nsmallest(10, distances, key=lambda x: x[1])
print top_distances
def getNodeNChildNodeDistance(self, neighbors, neighborsList, location, n): if (n.visited): return neighborsList else: distance = Distance.distance_on_unit_sphere(float(location[0]), float(location[1]), float(n.location[0]), float(n.location[1])) * 3960 n.visited = True if len(neighborsList) < neighbors: neighborsList[n.locationid] = distance else: neighborsList = self.getNearestNeighbors( neighborsList, distance, n) return neighborsList
def search(img_path):
ulbp_f = ulbp.ulbp(img_path)
ulbps = File_Operation.read_list_from_file('/Users/ligang/Documents/ulbp.txt')
distances = []
for ulbp_tuple in ulbps:
d = Distance.distance(ulbp_f, ulbp_tuple[1])
distances.append((ulbp_tuple[0], d))
top_distances = heapq.nsmallest(20, distances, key=lambda x: x[1])
dstDir = '/Users/ligang/Documents/Emilie/lbp_search_result'
img_set_dir = '/Users/ligang/Documents/Emilie/dress'
for top in top_distances:
shutil.copy2(os.path.join(img_set_dir, top[0]), os.path.join(dstDir, top[0]))
print top_distances
def search(img_path):
cf = CaculateColorVector.get_color_feature(img_path)
unlbp_feature = Uniform_LBP.ulbp(img_path)
cfs = File_Operation.read_list_from_file('/Users/ligang/Documents/cfs.txt')
ulbps = File_Operation.read_list_from_file('/Users/ligang/Documents/ulbp.txt')
distances = []
for cf_tuple, ulbp_tuple in zip(cfs, ulbps):
assert cf_tuple[0] == ulbp_tuple[0]
d_color = Distance.distance(cf, cf_tuple[1])
d_ulbp = Distance.distance(unlbp_feature, ulbp_tuple[1])
d = d_color + d_ulbp
distances.append((cf_tuple[0], d))
top_distances = heapq.nsmallest(20, distances, key=lambda x: x[1])
dstDir = '/Users/ligang/Documents/Emilie/colorlbp_search_result'
img_set_dir = '/Users/ligang/Documents/Emilie/dress'
for top in top_distances:
shutil.copy2(os.path.join(img_set_dir, top[0]), os.path.join(dstDir, top[0]))
print top_distances
def moveDist(address, addressPharm): # function to send the drone the distance it needs to fly
# also used for return trips
deliveryLat = Distance.latitude(address) # converts an address to its latitude equivalent
deliveryLong = Distance.longtitude(address) # converts an address to its longitude equivalent
pharmcacyLat = Distance.latitude(addressPharm)
pharmacyLong = Distance.longitude(addressPharm)
flyDist = Distance.conversion(deliveryLat, deliveryLong, pharmcacyLat, pharmacyLong)
# conversion finds the distance in miles between two longitudes and latitudes
drone.moveForward()
time.sleep(Distance.findtime(flyDist, speedofdrone)) # receives the time the drone must move for
def separate_path(all_orders, path, condition, params):
"""
将一个序列分离为满足条件的车辆路径
:type all_orders: list
:type path: list
:type condition: dict
:type params: dict
"""
assert len(all_orders) - 1 == len(path)
separate_number_list = []
start = all_orders[0] # 起点
v = params['velocity'] # 速度
stay_time = params['stay_time'] # 停留时间
path_time = 0 # 记录每车的总时间
path_len = len(path) # 路径长度，不包括起点
i = 0 # 路径中 node 的指针
pre = start
total_distance = 0
while i < path_len:
curr_node = all_orders[path[i]]
distance = Distance.get_distance(pre['lat'], pre['lng'], curr_node['lat'], curr_node['lng'])
time = distance / v
if path_time + time > condition["time"]:
# 重置迭代变量
if pre == start:
raise RuntimeError("经纬度{}离原点{}太远".format(curr_node, pre))
path_time = 0
pre = start
separate_number_list.append(i)
else:
pre = curr_node
path_time += time + stay_time
total_distance += distance
i += 1
separate_number_list.append(i)
return {"separate_number_list": separate_number_list, "distance": total_distance}
def getRandom() : return randint(0,15) startValue = 0.0 data = 'false' orderflag = 0 while True : data = serverflag.parseHtml() print data if data == 'true' : #distance = getRandom(); distance = sensor.getDistance() print distance if initialvalue.isIntialNotSet(): #if startValue == 0: startValue = distance #startValue = 15.0 initialvalue.setInitialValue(startValue) else: startValue = initialvalue.getInitialValue() percnt = ((startValue-distance)/startValue)*100 print percnt
file2_contents = file2.read().split('\n')
f2contents = file2_contents[1].split(',') # index 0 contains feature headers
data1 = []
data2 = []
for k in range(0, len(f1contents)):
try:
f1contents[k] = float(f1contents[k])
data1.append(f1contents[k])
except:
pass
for k in range(0, len(f2contents)):
try:
f2contents[k] = float(f2contents[k])
data2.append(f2contents[k])
except:
pass
indices = Distance.prune(data1, data2)
#print("Euclidean: " + str(Distance.euclidean_distance(data1, data2, indices)))
output.write(files[i] + "," + files[j] + "," + "euclidean" + "," + str(Distance.euclidean_distance(data1, data2, indices)) + "\n")
#print("City: " + str(Distance.city_distance(data1, data2)))
output.write(files[i] + "," + files[j] + "," + "city" + "," + str(Distance.city_distance(data1, data2)) + "\n")
#print("Chebychev: " + str(Distance.chebychev_distance(data1, data2)))
output.write(files[i] + "," + files[j] + "," + "chebychev" + "," + str(Distance.chebychev_distance(data1, data2)) + "\n")
#print("Cosine: " + str(Distance.cosine_difference(data1, data2)))
output.write(files[i] + "," + files[j] + "," + "cosine" + "," + str(Distance.cosine_difference(data1, data2)) + "\n")
#print("Correlation: " + str(Distance.correlation_distance(data1, data2)))
output.write(files[i] + "," + files[j] + "," + "correlation" + "," + str(Distance.correlation_distance(data1, data2)) + "\n")
def main(argv):
if len(argv) != 5:
print('USAGE: <native pdb file> <pdb file> <model limit> <output file prefix> <lrmsd criteria>')
sys.exit(2)
try: #TODO: add better checking here
native_in = str(argv[0])
file_in = str(argv[1])
nr_models = int(argv[2])
output_prefix = str(argv[3])
lrmsd_criteria = int(argv[4])
except:
print('USAGE: <native pdb file> <pdb file> <model limit> <output file prefix> <lrmsd criteria>')
sys.exit(2)
#Create lists of conformations
labels, nativeconformation, conformations = Parser.PDB(native_in, file_in, nr_models)
#Sort into positive and negative sets using lRMSD
withinlRMSD, morethanlRMSD = Distance.sortBylRMSDs(nativeconformation, conformations, lrmsd_criteria)
#output image of native graph
#nativeGraph = nx.Graph()
#curr_conf = nativeconformation[0]
#for j in range(len(curr_conf)-RES_DISTANCE):
# for k in range(j+RES_DISTANCE, len(curr_conf)):
# atom1 = curr_conf[j]
# atom2 = curr_conf[k]
# #add nodes to graph with labels
# nativeGraph.add_node(j)
# nativeGraph.node[j]['aminoAcid'] = labels[j]
# nativeGraph.add_node(k)
# nativeGraph.node[k]['aminoAcid'] = labels[k]
# #find euclidean distance between atoms
# d = Distance.euclideanDistance(atom1, atom2)
# #if less than BIN_CRITERIA, add edge
# if(d <= BIN_CRITERIA):
# nativeGraph.add_edge(j, k, distance=d)
#printGraph(nativeGraph, 'Output/PosGraphs/native')
#output graph attributes for each data set
#Note: removed newline='' from open() for linux
dt = time.strftime("_%Y%m%d-%H%M%S")
with open('Output/'+output_prefix+dt+'.csv', 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
writer.writerow(['num_edges', 'density','avg_degree','percent_endpoints','energy', 'second_eigen', 'unique_eigen', 'spectral_rad', 'inverse_product',
'link_impurity', 'neighborhood_impurity', 'avg_closeness', 'avg_clustering', 'small_worldness','eccentricity','diameter',
'radius','%central_nodes', '%Hydrophobic_center', 'near_native'])
#Positive Data Set
for i in range(len(withinlRMSD)):
graph = nx.Graph()
curr_conf = withinlRMSD[i]
for j in range(len(curr_conf)-RES_DISTANCE):
for k in range(j+RES_DISTANCE, len(curr_conf)):
atom1 = curr_conf[j]
atom2 = curr_conf[k]
#add nodes to graph with labels
graph.add_node(j)
graph.node[j]['aminoAcid'] = labels[j]
if(labels[j] in HPHOBIC):
graph.node[j]['hydro'] = 'phobic'
else:
graph.node[j]['hydro'] = 'philic'
graph.add_node(k)
graph.node[k]['aminoAcid'] = labels[k]
if(labels[k] in HPHOBIC):
graph.node[k]['hydro'] = 'phobic'
else:
graph.node[k]['hydro'] = 'philic'
#find euclidean distance between atoms
d = Distance.euclideanDistance(atom1, atom2)
#if less than BIN_CRITERIA, add edge
if(d <= BIN_CRITERIA):
graph.add_edge(j, k, distance=d)
##FOR TESTING ONLY
#printGraph(graph, 'Output/PosGraphs/pos_'+str(i))
#################
#once graph is done, create attribute vector
attributes = graphAttributes(graph)
##FOR TESTING##
#attributes = []
#if(not nx.is_connected(graph)):
# print("Graph " + i + "from within is not connected")
# sys.exit(2)
#else:
# attributes.append(nx.is_connected(graph))
#add 1 to the end since near native
attributes.append(1)
#and output to file as row
writer.writerow(attributes)
#Negative Data Set
for i in range(len(morethanlRMSD)):
graph = nx.Graph()
curr_conf = morethanlRMSD[i]
for j in range(len(curr_conf)-RES_DISTANCE):
for k in range(j+RES_DISTANCE, len(curr_conf)):
atom1 = curr_conf[j]
atom2 = curr_conf[k]
#add nodes to graph with labels
graph.add_node(j)
graph.node[j]['aminoAcid'] = labels[j]
if(labels[j] in HPHOBIC):
graph.node[j]['hydro'] = 'phobic'
else:
graph.node[j]['hydro'] = 'philic'
graph.add_node(k)
graph.node[k]['aminoAcid'] = labels[k]
if(labels[k] in HPHOBIC):
graph.node[k]['hydro'] = 'phobic'
else:
graph.node[k]['hydro'] = 'philic'
#find euclidean distance between atoms
d = Distance.euclideanDistance(atom1, atom2)
#if less than BIN_CRITERIA, add edge
if(d <= BIN_CRITERIA):
graph.add_edge(j, k, distance=d)
##FOR TESTING ONLY
#printGraph(graph, 'Output/NegGraphs/neg_'+str(i))
#################
#once graph is done, create attribute vector
attributes = graphAttributes(graph)
##FOR TESTING ONLY##
#if(not nx.is_connected(graph)):
# print("Graph " + i + "from morethan is not connected")
# sys.exit(2)
#else:
# attributes.append(nx.is_connected(graph))
#add 0 to the end since decoy
attributes.append(0)
#and output to file as row
writer.writerow(attributes)
print("ATTRIBUTES HAVE BEEN OUTPUTTED")
def main(argv):
if len(argv) != 3:
print("Usage: <native.pdb> <decoys.pdb> <output.csv>")
sys.exit(2)
try:
native_in = argv[0]
file_in = argv[1]
#nr_models = m
output_file = argv[2]
except:
print("Usage: <native.pdb> <decoys.pdb> <output.csv>")
sys.exit(2)
#Count atoms and calculate LCS if needed
native_atoms = Parser.countResidues(native_in)
decoy_atoms = Parser.countResidues(file_in)
if len(native_atoms) != len(decoy_atoms):
print("Unequal, find longest common sequence")
native_result, decoy_result = Parser.lcs(native_atoms, decoy_atoms)
print("New length: " + str(len(native_result)))
else:
native_result = []
decoy_result = []
#Read and store native conformation
nativelabels, nativeconformation = Parser.readConformations(str(native_in), 1, native_result)
#Read decoys and store how many are within distance, morethan distance
#using criteria{2,4}
criteria = [2.0000000,4.0000000]
models = 0
atoms = []
output_data = []
currConf = []
within2 = 0
morethan2 = 0
within4= 0
morethan4 = 0
with open(file_in, 'r') as f:
for line in f:
#while models < nr_models:
#line = f_read.readline()
splt = line.split()
if splt[0] == 'MODEL':
atoms = []
currConf = []
alpha_carbons = 1
elif splt[0] == 'ATOM':
if(splt[2] == 'CA'):
if(len(decoy_result) == 0 or (str(splt[3]), int(splt[5])) in decoy_result):
atoms.append((float(splt[6]), float(splt[7]), float(splt[8])))
elif splt[0] == 'TER':
if(len(atoms) > 0):
currConf.append(atoms)
models += 1
distance = Distance.lrmsd(nativeconformation[0], currConf[0])
#output_data.append([distance])
if distance <= criteria[0]:
within2 += 1
within4 += 1
else:
morethan2 += 1
if distance <= criteria[1]:
within4 +=1
else:
morethan4 += 1
#Output results in table with protein name, lcs length, number within/morethan for each criteria
output_data.append(native_in[5:-4])
output_data.append(len(nativeconformation[0]))
output_data.append(within2+morethan2)
output_data.append(within2)
output_data.append(morethan2)
output_data.append(within4)
output_data.append(morethan4)
with open(output_file, 'a+') as csvfile:
writer = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
if(csvfile.readline() == ""):
writer.writerow(["Protein", "Num CA", "Num Confs", "Within 2", "Morethan 2", "Within 4", "Morethan 4"])
writer.writerow(output_data)
#for d in output_data:
# writer.writerow(d)
print("Completed")
