def makeTree(data, label, attributes, level, max_depth):
level += 1
default = majorityLabel(data[:, label])
# Max depth check
if max_depth == level:
return Tree.Tree(default, level)
if len(attributes) <= 0:
return Tree.Tree(default, level)
elif sameLabel(data[:, label]):
return Tree.Tree(default, level)
else:
bestAttribute = chooseAttr(data, label, attributes)
tree = Tree.Tree(bestAttribute, level)
newAttributes = attributes
newAttributes.remove(bestAttribute)
for i in range(1, 11):
examples = getExamples(data, bestAttribute, i)
if examples[:, 1].size == 0:
tree.addChild(Tree.Tree(default, level + 1), i)
else:
tree.addChild(
makeTree(examples, label, newAttributes, level, max_depth),
i)
return tree
def fromSKLearn(self, forest, roundSplit=False):
# TODO: Add AdaBoostRegressor
# TODO: Add RandomForestRegressor
if (issubclass(type(forest), AdaBoostClassifier)):
sumW = sum([w for w in forest.estimator_weights_])
# https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/weight_boosting.py#L297
# see: decision_function
if (forest.algorithm == "SAMME"):
for e, w in zip(forest.estimators_, forest.estimator_weights_):
tree = Tree.Tree()
tree.fromSKLearn(e, roundSplit, "SAMME", w / sumW)
self.trees.append(tree)
else:
for e in forest.estimators_:
tree = Tree.Tree()
tree.fromSKLearn(e, roundSplit, "SAMME.R", 1.0 / sumW)
self.trees.append(tree)
elif (issubclass(type(forest), RandomForestClassifier)) or (issubclass(
type(forest), ExtraTreesClassifier)):
for e in forest.estimators_:
tree = Tree.Tree()
tree.fromSKLearn(e, roundSplit, "RandomForest",
1.0 / len(forest.estimators_))
self.trees.append(tree)
else:
raise NotImplementedError(
"fromSKLearn() is not implemented for class ", type(forest))
def fit(self, training_file, output_file, word_map_file='word_map.bin'):
if not os.path.exists(word_map_file):
tr.build_word_map(training_file, word_map_file)
self.word_map_file = word_map_file
self.trees = tr.load_trees(self.data_folder + training_file,
self.word_map_file)
self.num_words = len(tr.load_word_map(self.word_map_file))
self.rntn = RNTN.RNTN(self.vect_dim, self.output_dim, self.num_words,
self.mini_batch_size)
self.sgd = SGD.SGD(self.rntn, self.learning_rate, self.mini_batch_size)
for e in range(self.optim_epochs):
# Fit model
# --------------------------
start = time.time()
print "Running epoch %d" % e
self.sgd.optimize(self.trees)
end = time.time()
print "\nTime per epoch : %f" % (end - start)
# Save model specifications
with open(output_file, 'w+') as fid:
pickle.dump([(i, self.values[i]) for i in self.args][1:], fid)
pickle.dump(self.sgd.cost_list, fid)
pickle.dump(self.rntn.stack, fid)
def buildDecisionTree(self, data, threshold, attributes): if self.binary: root = Tree.BinarySplitTree() else: root = Tree.MultiSplitTree() mostLabel = self.mostLabel(data) if self.isPure(data) or len(attributes) == 0 or data.shape[0] < threshold: root.isLeaf = True root.label = mostLabel return root else: root.isLeaf = False root.entropy = self.entropy(data) root.mostLabel = mostLabel attribute, value = self.bestAttribute(data, root.entropy, attributes) root.attribute = attribute newAttributes = attributes[:] newAttributes.remove(attribute) if self.binary: root.value = value root.leftChild = self.getChild(data.loc[data[attribute] < value], threshold, newAttributes, mostLabel) root.rightChild = self.getChild(data.loc[data[attribute] >= value], threshold, newAttributes, mostLabel) else: for newValue in data[attribute].unique(): dataset = data.loc[data[attribute] == newValue] child = self.buildDecisionTree(dataset, threshold, newAttributes) child.value = newValue root.children.append(child) return root
def __generate_test_case(self):
tmp_path = self.p.get_current_test_case_path() + SETTINGS.get(
"test_case_folder_name") + "_" + str(
self.p.get_current_test_case_nb()) + ".test_case"
if self.verbose:
print("Current path: " +
misc.color(self.p.get_current_test_case_path(), "cyan"))
if misc.check_file(tmp_path, self.verbose):
if self.verbose:
print("Parsing existing test case: " +
misc.color(tmp_path, "cyan"))
self.tree.read_test_case(tmp_path)
else:
if self.verbose:
print("Create new test case: " + misc.color(
SETTINGS.get("test_case_folder_name") +
".test_case", "cyan"))
counter = 0
while not self.tree.generate(self.verbose):
print(counter)
self.tree = Tree(
SETTINGS.get("template_path") +
SETTINGS.get("template_file_name"))
counter += 1
if self.verbose:
self.tree.print_current_node()
self.tree.write_test_case(tmp_path)
def check_perms(s, set, handles, **kwargs):
ask_flag = kwargs.get('Ask', True)
if not ask_flag:
if not MyUtil.get_yn('!!! Warning !!! ask = False: Will not ask to make changes, okay? Enter N to Exit, Y to Continue:'):
print('exiting...')
sys.exit(0)
for handle in handles:
if 'Document-' in handle:
fd = DCC.prop_get(s, handle, InfoSet = 'DocBasic')
elif 'Collection-' in handle:
fd = DCC.prop_get(s, handle, InfoSet = 'CollData')
else:
fd = DCC.prop_get(s, handle, InfoSet = 'Title')
fd['permissions'] = DCC.prop_get(s, handle, InfoSet = 'Perms')
# print(fd['handle'], ':', fd['title'])
print('\n>>>>>>>>>>>>>> DCC Information <<<<<<<<<<<<<<')
if 'Document-' in handle:
DCC.print_doc_basic(fd)
elif 'Collection-' in handle:
DCC.print_coll_data(fd)
else:
print('Not Document or Collection:', handle, ':', fd['title'])
print('\n\tDoc Properties URL: ',Tree.url_view(handle))
print('\tPermissions URL: ',Tree.url_perm(handle))
print('\tGet Document URL: ',Tree.url_access(handle))
print()
fix_objact(s, fd, handle, set, **kwargs)
fix_permact(s, fd, handle, set, **kwargs)
def __init__(self, actionList, learningRate, discountFactor):
self.actionList = actionList
self.learningRate = learningRate
self.discountFactor = discountFactor
#Contains information of what states+actions lead to what states
self.TransitionTable = {}
#Contains Q-values for state-action pairs
self.QTable = {}
#Contains average reward for state-action pairs
self.ETable = {}
#A tree structure of rewarded sequences. Each sequence is stored in reverse to quickly find equivalent sequences
self.rewardedSequences = Tree()
#Same as above but not stored in reverse
self.rewardedSequencesForward = Tree()
self.rewardedSequencesForward.node = None
#A set with all rewarded sequences
self.rewardedSet = set()
#A set with all punished sequences
self.inputSet = set()
def Format(self, data):
rootNode = Tree.Node(nodeLabel='[' + '/'.join(self.groupByAttrs) + ']',
children=[],
nodesDict={})
for item in data:
node = rootNode
for groupByAttr in self.groupByAttrs:
groupByValue = str(rec_getattr(item, groupByAttr))
nodesDict = node.nodesDict
oldNode = node
node = nodesDict.get(groupByValue, None)
if not node:
node = Tree.Node(nodeLabel=groupByValue,
children=[],
nodesDict={})
oldNode.nodesDict[groupByValue] = node
oldNode.children.append(node)
node.children.append(item)
print Tree.FormatAsTree(data=rootNode,
labelAttr='nodeLabel',
labelFunc=self.labelFunc,
childrenAttr='children',
useVt100LineDrawingChars=True)
return ''
def fit(self, training_file, output_file, word_map_file='word_map.bin'):
if not os.path.exists(word_map_file):
tr.build_word_map(training_file, word_map_file)
self.word_map_file = word_map_file
self.trees = tr.load_trees(self.data_folder + training_file, self.word_map_file)
self.num_words = len(tr.load_word_map(self.word_map_file))
self.rntn = RNTN.RNTN(self.vect_dim, self.output_dim, self.num_words, self.mini_batch_size)
self.sgd = SGD.SGD(self.rntn, self.learning_rate, self.mini_batch_size)
for e in range(self.optim_epochs):
# Fit model
# --------------------------
start = time.time()
print "Running epoch %d" % e
self.sgd.optimize(self.trees)
end = time.time()
print "\nTime per epoch : %f" % (end-start)
# Save model specifications
with open(output_file, 'w+') as fid:
pickle.dump([(i, self.values[i]) for i in self.args][1:], fid)
pickle.dump(self.sgd.cost_list, fid)
pickle.dump(self.rntn.stack, fid)
def build_ann(exset, num_hidden, weight_decay_coef, num_iter):
learning_rate = 0.01
num_examples = exset.shape[0]
num_nodes_l1 = exset.shape[1] - 2
num_nodes_l3 = 1
if num_hidden != 0:
num_nodes_l2 = num_hidden
num_nodes_per_layer = [num_nodes_l1, num_nodes_l2, num_nodes_l3]
else:
num_nodes_per_layer = [num_nodes_l1, num_nodes_l3]
tree = Tree(num_nodes_per_layer)
if num_iter < 1: # train until convergence
eps = 0.01 # convergence test
delta_w = 1 # initialize delta larger than eps
iteration = 0
while delta_w > eps:
w_start = tree.compute_all_weights()
w_final = iterate_example_set(tree, exset, learning_rate,
weight_decay_coef)
iteration += np.shape(exset)[0]
delta_w = abs(w_start - w_final)
print iteration
else:
iteration = 0
while iteration < num_iter:
iterate_example_set(tree, exset, learning_rate, weight_decay_coef)
iteration += np.shape(exset)[0]
return tree
def addRewardedSequence(self, sequence, reward=None, action=None):
nextChar = sequence[-1]
branch = self.rewardedSequences
for x in range(len(sequence)):
char = nextChar
if x < len(sequence) - 1:
nextChar = sequence[-(x + 2)]
else:
nextChar = 1
if char not in branch.connections:
branch.connections[char] = Tree()
branch.connections[char].node = False
if nextChar == 1:
branch.connections[char].node = True
branch = branch.connections[char]
nextChar = sequence[0]
branch = self.rewardedSequencesForward
for x in range(len(sequence)):
char = nextChar
if x < len(sequence) - 1:
nextChar = sequence[x + 1]
else:
nextChar = 1
if char not in branch.connections:
branch.connections[char] = Tree()
branch.connections[char].node = None
if nextChar == 1:
branch.connections[char].node = (action, reward)
branch = branch.connections[char]
def AddToTree(board19, Candidate, player):
temp = []
if (player == 10):
enemy = 20
else:
enemy = 10
for i in board19:
temp += [i]
evalTree = Tree(temp)
for i in Candidate:
for q in i[1]:
tempboard = genNewBoard(temp, [i[0], q], player)
tempTree = genTree(tempboard)
tempCandidate = CandidateMoves(tempboard, enemy)
for a in tempCandidate:
for b in a[1]:
temp2 = genNewBoard(tempboard, [a[0], b], enemy)
temp2Tree = genTree(temp2)
Candidate2 = CandidateMoves(temp2, player)
for c in Candidate2:
for d in c[1]:
temp3 = genNewBoard(temp2, [c[0], d], player)
temp3Tree = genTree(temp3)
temp2Tree.AddSuccessor(temp3Tree)
tempTree.AddSuccessor(temp2Tree)
evalTree.AddSuccessor(tempTree)
return evalTree
def main():
array = list(range(7))
array = [1, 2, 4, 4, 5, 6, 7, 8]
tree = Tree(array)
tree.print_tree()
print(validate_BST(tree.root))
def DisplayDevicesAsTree(self, vm, attrs, useVt100Chars):
"""
Display virtual devices as a tree.
"""
def FormatRecord(record):
return ', '.join(
[str(rec_getattr(record, attr)) for attr in attrs]).rstrip()
def GetDevices(obj):
if isinstance(obj, Tree.Node):
return [
device for device in vm.GetAllDevices()
if device.controller is None
]
elif hasattr(obj, 'managedObject'):
if hasattr(obj.managedObject, 'device'):
return [
vm.GetDevice(key) for key in obj.managedObject.device
]
else:
return []
rootNode = Tree.Node(nodeLabel=vm.name)
print Tree.FormatAsTree(data=rootNode,
labelAttr='nodeLabel',
labelFunc=FormatRecord,
childrenFunc=GetDevices,
useVt100LineDrawingChars=useVt100Chars)
def compileSubroutineBody(self, subTreeNode):
token = self.tokenizer.getNextToken()
if token.getToken() != "{":
raise ValueError(
"Expeced \'{\' at start of subroutine body, but received: " +
token.getToken())
subTreeNode.addChild(token)
# handle 0 or more varDecs
nextToken = self.tokenizer.peekNextToken()
while nextToken.getToken() == 'var':
varDecSubTreeNode = Tree.Node("varDec", subTreeNode.depth + 1)
self.compileVarDec(varDecSubTreeNode)
subTreeNode.addChildTree(varDecSubTreeNode)
nextToken = self.tokenizer.peekNextToken()
# handle 0 or more statements
statementsSubTreeNode = Tree.Node("statements", subTreeNode.depth + 1)
self.compileStatements(statementsSubTreeNode)
subTreeNode.addChildTree(statementsSubTreeNode)
token = self.tokenizer.getNextToken()
if token.getToken() != '}':
raise ValueError(
"Expeced \'}\' at end of subroutine body, but got " +
token.getToken() + " instead.")
subTreeNode.addChild(token)
return subTreeNode
def background():
"""Creates the general background scenery"""
#Creates random clouds in the sky
for r in range(0,cloudNum):
#Initialize variables
x = randint(-100,1200)
y = randint(-20,100)
width = randint(60,90)
height = randint(20,50)
cloudColor = choice(["gray92","gray95","gray98"])
#Create lots of ovals to represent clouds
for i in range(0,20):
x1 = x - randint(1, width)
y1 = y - randint(1, height)
x2 = x + randint(1, width)
y2 = y + randint(1, height)
oval = screen.create_oval(x1, y1, x2, y2, fill=cloudColor, outline=cloudColor)
#Ground behind the trees
screen.create_oval(-100,320,1100,600, fill ="gainsboro", outline ="gainsboro")
#Trees from Tree.py
Tree.tree(screen)
#Snowy Ground
screen.create_rectangle (0,500,1000,800,fill="snow",outline="snow")
screen.create_polygon (0,500,50,500,100,450,250,470,400,430,500,450,650,420,700,450,800,460,1020,470,1000,600, fill="snow",outline="snow",smooth="true")
#Scorebox
screen.create_rectangle( 320,100,680,350, fill="#80d5ff", outline="white", width="8")
def do_generate(self, arg):
if self.tree is None:
print("A template must be parsed first")
return
if not self.p.check_path(self.auto, self.verbose):
print("file hierarchy is incomplete!")
return
while True:
start = time.time()
self.__generate_test_case()
end = time.time()
with open(self.p.get_experiment_path() + "time", "a") as f:
f.write(str(end - start) + "\n")
print("time: " + str(end - start))
while True:
if self.verbose:
print("generate test artifact")
self.__generate_test_artifact()
if not self.p.next_test_artifact():
if not self.p.next_path():
return
break
self.tree = Tree(
SETTINGS.get("template_path") +
SETTINGS.get("template_file_name"))
def compileWhileStatement(self, subTreeNode):
token = self.tokenizer.getNextToken()
if token.getToken() != "while":
raise ValueError("Expected \'while\'.")
subTreeNode.addChild(token)
token = self.tokenizer.getNextToken()
if token.getToken() != "(":
raise ValueError("Expected \'(\'.")
subTreeNode.addChild(token)
exprSubTreeNode = Tree.Node("expression", subTreeNode.depth + 1)
self.compileExpression(exprSubTreeNode)
subTreeNode.addChildTree(exprSubTreeNode)
token = self.tokenizer.getNextToken()
if token.getToken() != ")":
raise ValueError("Expected \')\'.")
subTreeNode.addChild(token)
token = self.tokenizer.getNextToken()
if token.getToken() != "{":
raise ValueError("Expected \'{\'.")
subTreeNode.addChild(token)
whileStatementsSubTreeNode = Tree.Node("statements", subTreeNode.depth + 1)
self.compileStatements(whileStatementsSubTreeNode)
subTreeNode.addChildTree(whileStatementsSubTreeNode)
token = self.tokenizer.getNextToken()
if token.getToken() != "}":
raise ValueError("Expected \'}\'.")
subTreeNode.addChild(token)
return subTreeNode
def isBalanced(bt):
if bt is None:
return True
d = Tree.getHeight(bt.left) - Tree.getHeight(bt.right)
if abs(d) > 1:
return False
else:
return isBalanced(bt.left) and isBalanced(bt.right)
def main():
array = list(range(7))
tree = Tree(array)
tree.print_tree()
print(
get_first_common_ancestor(tree.root.left_child.left_child,
tree.root.right_child))
def isBalanced(bt):
if bt is None:
return True
d = Tree.getHeight(bt.left) - Tree.getHeight(bt.right)
if abs(d) > 1:
return False
else:
return isBalanced(bt.left) and isBalanced(bt.right)
def __init__(self, iface,user): # Save reference to the QGIS interface self.iface = iface self.message=None self.tree=Tree(user) self.message="selected" self.progressBar=ProgressBarWindow(True,True)# apro la progress bar con due barre self.progressBar.setMaximumOverall(4) self.ui_tree=MainWindowAlbero(iface,self.tree)
def fib_tree(n):
if n == 1:
return Tree(0)
elif n == 2:
return Tree(1)
else:
left = fib_tree(n-2)
right = fib_tree(n-1)
return Tree(left.label + right.label, (left, right))
def print_to_file(self, folder, is_pruned):
for i in range(0, len(self.trees)):
# Tree.print_tree(self.trees[i].root_node)
if (is_pruned):
Tree.write_tree_to_file(self.pruned_trees[i].root_node, i + 1,
folder, is_pruned)
else:
Tree.write_tree_to_file(self.trees[i].root_node, i + 1, folder,
is_pruned)
def Read(self):
# yarab.program()
# print(yarab.token_type)
# print(yarab.token_value)
# print(yarab.error_type)
# print(yarab.error_type)
# if yarab.error_type != "":
# Tree.process(token_value,token_type)
# else:
# print('error')
token_value = []
string_t_value = ''
token_type = []
string_t_type = ''
bool_colon_found = 0
j = 0
token_len = 0
set = stat('test.txt').st_size == 0
if set != True:
with open('test.txt') as fp:
for cnt, line in enumerate(fp):
print(line)
for i in line:
if i == ' ':
pass
elif i == ',':
bool_colon_found = 1
elif bool_colon_found == 0:
string_t_value += i
elif (bool_colon_found == 1) and (i != '\n'):
string_t_type += i
token_value.append(string_t_value)
token_type.append(string_t_type)
token_len = len(token_type)
bool_colon_found = 0
string_t_value = ''
string_t_type = ''
# print(token_value)
# print(token_type)
yarab.token_value = token_value
yarab.token_type = token_type
yarab.token_len = token_len
yarab.program()
if yarab.error_type == "":
Tree.process(token_value, token_type)
else:
QMessageBox.about(self, "error Messsage", yarab.error_type)
print(yarab.error_type)
# //give me the msg box for error_type
else:
error_type = "the file is empty"
QMessageBox.about(self, "error Messsage", error_type)
print(error_type)
class Page(Base.Page):
def childFactory(self, ctx, seg):
set = {
"Edit": Edit.editPage,
"Groups": Group.editGroups,
"GroupMod": Group.editGroupsByGroup,
"GroupAdd": Group.addGroups,
"Delete": Delete.deletePage,
"Add": Add.addPage,
"DomainAdd": Domains.addDomain,
"DomainDel": Domains.delDomain,
"Bulk": Bulk.bulkUsers,
"Failed": Base.FailurePage
}
if seg in set.keys():
return set[seg](self.avatarId, db=self.db)
else:
return Page(self.avatarId, self.db, seg)
def render_editContent(self, ctx, data):
return ctx.tag[
tags.h2[self.text.userManagement],
tags.p[self.text.usersBeginInstruction],
tags.br,
tags.img(src="/images/truck.png", alt="Bulk", style="vertical-align: middle"),
" ",
tags.a(href="Bulk/")["Bulk modifications"]
]
def render_treeView(self, ctx, data):
try:
T = Tree.Tree("r", "Domains")
l = Tree.retrieveTree(Settings.LDAPServer, Settings.LDAPManager, Settings.LDAPPass, 'o='+Settings.LDAPBase)
flatL = Tree.flattenTree(l, 'o='+Settings.LDAPBase)
flatL.sort()
self.flatFil = []
except Exception, e:
flatL = []
Utils.exceptionOccured(e)
if not self.avatarId.isAdmin:
for nod in flatL:
for d in self.avatarId.domains:
if (d in nod):
self.flatFil.append(nod)
elif not "dm=" in nod:
self.flatFil.append(nod)
else:
self.flatFil = flatL
for node in self.flatFil:
Tree.addPath(node, T)
return ctx.tag[
tags.div(id="TreeCont")[Tree.StanTree(T, self.cid)],
]
def compileIfStatement(self, subTreeNode):
token = self.tokenizer.getNextToken()
if token.getToken() != "if":
raise ValueError("Expected if.")
subTreeNode.addChild(token)
token = self.tokenizer.getNextToken()
if token.getToken() != "(":
raise ValueError("Expected \'(\'.")
subTreeNode.addChild(token)
ifExprSubTreeNode = Tree.Node("expression", subTreeNode.depth + 1)
self.compileExpression(ifExprSubTreeNode)
subTreeNode.addChildTree(ifExprSubTreeNode)
token = self.tokenizer.getNextToken()
if token.getToken() != ")":
raise ValueError("Expected \')\'.")
subTreeNode.addChild(token)
token = self.tokenizer.getNextToken()
if token.getToken() != "{":
raise ValueError("Expected \'{\'.")
subTreeNode.addChild(token)
ifStatementsSubTreeNode = Tree.Node("statements",
subTreeNode.depth + 1)
self.compileStatements(ifStatementsSubTreeNode)
subTreeNode.addChildTree(ifStatementsSubTreeNode)
token = self.tokenizer.getNextToken()
if token.getToken() != "}":
raise ValueError("Expected \'}\'.")
subTreeNode.addChild(token)
nextToken = self.tokenizer.peekNextToken()
if nextToken.getToken() == "else":
token = self.tokenizer.getNextToken()
subTreeNode.addChild(token)
token = self.tokenizer.getNextToken()
if token.getToken() != "{":
raise ValueError("Expected \'{\'.")
subTreeNode.addChild(token)
elseStatementsSubTreeNode = Tree.Node("statements",
subTreeNode.depth + 1)
self.compileStatements(elseStatementsSubTreeNode)
subTreeNode.addChildTree(elseStatementsSubTreeNode)
token = self.tokenizer.getNextToken()
if token.getToken() != "}":
raise ValueError("Expected \'}\'.")
subTreeNode.addChild(token)
return subTreeNode
def draw_picture():
# window
wn.clearscreen()
wn.bgcolor("light blue")
# sun
sun_size_range = [100,200,150,125,175]
random_number = random.randint(0,4)
sun_size = sun_size_range[random_number]
sun_pos_range = [150 ,100, 0, -100, -150]
sun_pos = sun_pos_range[random_number]
sun = Sun()
sun.pen.penup()
sun.pen.goto(sun_pos, 100)
sun.draw(sun_size, "yellow")
# trees
positions = [-250, -100, 0, 100, 200]
sizes = [100, 150, 200, 125, 175]
tree_amount_range = [1,3,4,5,6,]
random_number = random.randint(0,4)
tree_amount = tree_amount_range[random_number]
random_track = 0
for i in range(tree_amount):
random_number = random.randint(0, 4)
random_track = random_number
while (random_number == random_track):
random_number = random.randint(0, 4)
random_position = positions[random_number]
tree_size = sizes[random_number]
tree = Tree()
tree.pen.goto(random_position, -300)
tree.draw(tree_size)
# Houses
house = House()
house.pen.penup()
house.pen.goto(-300, -300)
size = [100, 50, 200, 25, 75]
space = [50, 100, 10, 25, 75]
house_amount_range = [1,2,3,4,5]
random_number = random.randint(0,4)
house_amount = house_amount_range[random_number]
color = ["blue", "orange", "cyan", "purple", "pink"]
for i in range(house_amount):
random_number = random.randint(0, 4)
house_color = color[random_number]
house_size = size[random_number]
house_space = space[random_number]
house.draw(house_size, house_color, house_space)
wn.listen()
wn.onkeypress(draw_picture, 'Up')
wn.mainloop()
def createTreeForRule(self, rule):
children = []
for child in rule._alpha:
if child._type == 'Sigma':
children.append(Tree.Tree("Basic", child, (), [], self))
else:
# DEBUG: if here with a bug, you should consider that there might be a problem with the rules of the extended child"
children.append(Tree.Tree("Complex", child, [], [], self))
return Tree.Tree("Complex", rule._A, rule, children, self)
def render_treeView(self, ctx, data):
try:
T = Tree.Tree("r", "Domains")
l = Tree.retrieveTree(Settings.LDAPServer, Settings.LDAPManager, Settings.LDAPPass, 'o='+Settings.LDAPBase)
flatL = Tree.flattenTree(l, 'o='+Settings.LDAPBase)
flatL.sort()
self.flatFil = []
except Exception, e:
flatL = []
Utils.exceptionOccured(e)
def check_perms(s, set, handle, treename, **kwargs):
ask_flag = kwargs.get('Ask', True)
if not ask_flag:
if not MyUtil.get_yn('!!! Warning !!! ask = False: Will not ask to make changes, okay? Enter N to Exit, Y to Continue:'):
print('exiting...')
sys.exit(0)
tr = Tree.return_tree(s, handle, treename)
handles = Tree.get_flat_tree(tr)
for handle in handles:
fix_set(s, handle, set, **kwargs)
def __init__(self, type, key_words, secondary_words, excluding_words):
self.type_name = type
key_words_tree = Tree.AVLTree()
self.key_words = key_words_tree.insert_array(key_words)
secondary_words_tree = Tree.AVLTree()
self.secondary_words = secondary_words_tree.insert_array(
secondary_words)
excluding_Words_tree = Tree.AVLTree()
self.excluding_words = excluding_Words_tree.insert_array(
excluding_words)
def lightrun(self, list_surface=[Surface()], light_in=Light(), i_t=1):
# i_t is reflection times and trans times
# this function will simulate the ray, and return a tree and list_surface
tree_light = Tree()
tree_light.add(light_in)
# this will be a for command for i_t
for i in range(i_t):
# calcute for list_surface
# get myQueue length
q_length = len(tree_light.myQueue)
# run over myQueue,to add new light in tree, but we build a new list, queue is dynamic
# select light
list_light_temp = []
for index in range(q_length):
light_temp = tree_light.myQueue[index].elem
list_light_temp.append(light_temp)
for light_temp in list_light_temp:
if light_temp == None:
# no light to reflect
tree_light.add(None)
tree_light.add(None)
else:
# get p,t,surface
list_mpts = self.get_mp_t0_and_surface(
list_surface, light_temp)
mp_t0 = list_mpts[0]
surface_m = list_mpts[1]
# judge if surface is None
if surface_m == None:
# no reflect and transmisson
tree_light.add(None)
tree_light.add(None)
else:
# set the current light_in time
# find light_temp index in tree, through it's dynamic, bud no influence, cause if light_temp is finish , we dont need that
for i in range(len(tree_light.myQueue)):
if tree_light.myQueue[i].elem == light_temp:
index = i
tree_light.myQueue[index].elem.set_t(
np.arange(0, mp_t0[3] + mp_t0[3] / 10,
mp_t0[3] / 10))
# set the point in surface is meeting point to save the value to plot
list_surface_index = list_surface.index(surface_m)
# mp = [mp_t0[0], mp_t0[1], mp_t0[2]]
# list_surface[list_surface_index].set_p(np.array(mp))
# calcute trans and ref
light_t_temp = self.transmisson2(surface_m, light_temp)
light_r_temp = self.reflection(surface_m, light_temp)
# add to tree
tree_light.add(light_t_temp)
tree_light.add(light_r_temp)
return [tree_light, list_surface]
def compileSubroutineDec(self, subTreeNode):
# handle constuctor, function, method
token = self.tokenizer.getNextToken()
if token.getToken() not in ['constructor', 'function', 'method']:
raise ValueError(
"Subroutine declaration must begin with \'constructor\', \'function\', \'method\'."
)
subTreeNode.addChild(token)
# handle 'void' | type
token = self.tokenizer.getNextToken()
if token.getToken() == "void":
subTreeNode.addChild(token)
elif self.validateType(
token): # if not validateType, then we necessarily throw error
subTreeNode.addChild(token)
# handle subRoutineName
token = self.tokenizer.getNextToken()
self.validateSubroutineName(token)
subTreeNode.addChild(token)
# handle '('
token = self.tokenizer.getNextToken()
if token.getToken() != '(':
raise ValueError("Expected \'(\' before parameter list.")
subTreeNode.addChild(token)
# handle parameterList
pListSubTreeNode = Tree.Node("parameterList", subTreeNode.depth + 1)
self.compileParameterList(
pListSubTreeNode
) # needs to printed like <parameterList></paremeterList> (even if no children)
subTreeNode.addChildTree(pListSubTreeNode)
# handle ')'
token = self.tokenizer.getNextToken()
if token.getToken() != ')':
raise ValueError("Expected \')\' after parameter list, but got " +
token.getToken() + " instead.")
subTreeNode.addChild(token)
# handle subroutineBody
nextToken = self.tokenizer.peekNextToken()
if nextToken.getToken() != '{':
raise ValueError(
"Expeced \'{\' at start of subroutine body, but received: " +
nextToken.getToken())
subroutineBodySubTreeNode = Tree.Node("subroutineBody",
subTreeNode.depth + 1)
self.compileSubroutineBody(subroutineBodySubTreeNode)
subTreeNode.addChildTree(subroutineBodySubTreeNode)
return subTreeNode
def imprimir_predicciones(tree, test, header):
os.system("cls")
print("Imprimiendo...\n")
os.system("pause")
os.system("cls")
for row in test:
print("Actual: %s. Predicted: %s" %
(row[-1], Tree.print_prediction(Tree.classify(row, tree.root))))
print()
os.system("pause")
pass
def makeParseTreeNode(p, value):
'''Returns a Tree object containing
as children p[1:] and a value of value'''
toReturn = Tree()
for element in p[1:]:
if type(element) == type(toReturn):
toReturn.children.append(element)
toReturn.errors += element.errors
else:
# the element is not a tree. wrap it in a tree
newElement = Tree()
if isinstance(element, tuple):
newElement.value = element[0]
newElement.type = element[1]
else:
newElement.value = element
toReturn.children.append(newElement)
if isinstance(value, tuple):
toReturn.value = value[0]
toReturn.type = value[1]
else:
toReturn.value = value
if value == "error":
errorMessage = str(p[1][2]) + ": " + p[1][0]
toReturn.errors.append(errorMessage)
return toReturn
def optimize_branch_fast(tlobj, tprime, children_index_list):
"""
Quick optimization of subset of branches (indicated by <children_index_list>)
in tprime while using tlobj for parameters
children_index_list --- list of (i, j) indicating that we want to iteratively
refine the branch of node label i --- node label j
Most likely will be the 3 local branches at the new insertion point of a subtree.
For fast optimization, relax the branch length variation to 0.1.
Returns: final (positive) log likelihood of tprime
NOTE: this only changes branch lengths in tprime. tlobj not affected!!
NOTE: reversible_subtree_func currently cheats by only recalc-ing the
entries of parent and up, to ensure this works, I think making sure
copy_{S|P} are correct is important and therefore the two g() calls.
"""
g = MyMat.calc_likelihood
meat = scipy.optimize.fmin_l_bfgs_b
def reversible_subtree_func(copy_S, copy_P, parent, child, t_a):
#assert len(t_a) == 1
child.edge_length = t_a[0]
order = Tree.postorder_cheat_traverse(parent)
L_single = g(tlobj.single_model.gtr.R, copy_S, tlobj.log_freq_single, \
order, range(tlobj.ncol), tlobj.nnode, tlobj.ncol, tlobj.nbase)
L_paired = g(tlobj.paired_model.gtr.R, copy_P, tlobj.log_freq_paired, \
order, range(tlobj.ncol_p), tlobj.nnode_p, tlobj.ncol_p, tlobj.nbase_p)
ans = -(L_single.sum() + L_paired.sum())
return ans
# TODO: make this more efficient!
copy_S = tlobj.S.copy()
copy_P = tlobj.P.copy()
order = Tree.postorder_assign_then_traverse(tprime, None, False)
g(tlobj.single_model.gtr.R, copy_S, tlobj.log_freq_single, \
order, range(tlobj.ncol), tlobj.nnode, tlobj.ncol, tlobj.nbase)
g(tlobj.paired_model.gtr.R, copy_P, tlobj.log_freq_paired, \
order, range(tlobj.ncol_p), tlobj.nnode_p, tlobj.ncol_p, tlobj.nbase_p)
changed = True
while changed:
changed = False
for i, j in children_index_list:
parent = tprime.find_node_with_label(i)
child = tprime.find_node_with_label(j)
old_t_a = child.edge_length
func = lambda x: reversible_subtree_func(copy_S, copy_P, parent, child, x)
x, fx, d = meat(func, [old_t_a], approx_grad=True, \
bounds=[(1e-3, 10)], pgtol=1e-2)
# print "calling func {0}--{1} done".format(i,j), x, fx, d, x[0], old_t_a, abs(x[0] - old_t_a)
if d['warnflag'] != 0:
return None, None # handle this appropriately!
if abs(x[0] - old_t_a) > 0.1:
changed = True
return fx, tprime
def NNI(t):
"""
Randomly select an internal node to do NNI
alters the tree <t>
Returns: t, (new) order
"""
while True:
parent = random.choice(t.internal_nodes())
# choose one of the kids as target
# and another as sibling
target, sibling = random.sample(parent.child_nodes(), 2)
if target.is_leaf():
continue
else:
# select one children from target to swap w/ sibling
child = random.choice(target.child_nodes())
break
print >> sys.stderr, "NNI: parent {0}, target {1}, sibling {2}, child {3}".format(\
parent.label, target.label, sibling.label, child.label)
# swap child & sibling in tree
new_child_branch = child.edge_length + target.edge_length
new_sibling_branch = sibling.edge_length - target.edge_length
parent.remove_child(sibling)
target.remove_child(child)
parent.add_child(child, new_child_branch)
target.add_child(sibling, new_sibling_branch)
# obtain new order via postorder traversal (should be fast enough)
order = Tree.postorder_assign_then_traverse(t, None, do_assign=False)
return t, order
def optimize_branch_func(self, parent, child, t_a): """ <t_a> is the new branch length between <parent> --- <child> (t_a is dressed in an array...should always be just [t_a]) We can cheat & speed up likelihood calculation by re-using likelihods of subtrees below child and T' where T' is the subtree under root that does not contain parent/child Returns: POSITIVE sum of log likelihood (so we try to minimize it) currently wrapped up by ::optimize_branch:: NOTE: it is ::optimize_branch::'s responsibility to update self.order and self.like when all is done!!! p.s. potentialy speed-up by simultaneously optimizing two child branches of the same parent?? """ #assert len(t_a) == 1 g = MyMat.calc_likelihood child.edge_length = t_a[0] # TODO: make this more efficient! cheat_order = Tree.postorder_cheat_traverse(parent) L_single = g(self.single_model.gtr.R, self.S, self.log_freq_single, \ cheat_order, range(self.ncol), self.nnode, self.ncol, self.nbase) L_paired = g(self.paired_model.gtr.R, self.P, self.log_freq_paired, \ cheat_order, range(self.ncol_p), self.nnode_p, self.ncol_p, self.nbase_p) return -(L_single.sum() + L_paired.sum())
def __init__(self, msa, tree, single_model, paired_model, treat_gap_as_missing):
"""
Input:
msa --- MSA object (the alignment)
tree --- initially is the starting tree (dendropy.Tree)
single/paired model -- EvoModel.{single|paired}model objects
Also has the following attributes:
single_cols --- list of unpaired positions
paired_cols --- list of paired positions (i, j)
order --- postorder traversal (often changes! beware!)
like --- positive log likelihood (often changes! beware!)
nnode, ncol, nbase for single/paired parameters...
NOTE: ENFORCES tree TO BE BINARY
"""
self.msa = msa
self.tree = tree
self.single_model = single_model
self.paired_model = paired_model
self.treat_gap_as_missing = treat_gap_as_missing
self.log_freq_single = log(self.single_model.Frequency)
self.log_freq_paired = log(self.paired_model.Frequency)
self.single_cols = msa.single_cols()
self.paired_cols = msa.BP.items()
self.paired_cols.sort()
self.nnode = 2*msa.nseq + 1
self.ncol = len(self.single_cols)
self.nbase = 5
self.nnode_p = self.nnode
self.ncol_p = len(self.paired_cols)
self.nbase_p = 25
Tree.make_tree_binary(self.tree) # must preceded self.order!
self.order = Tree.postorder_assign_then_traverse(tree, list(msa.ids))
self.like = None # should be the positive log likelihood
self.S = None # likelihood matrix for single positions
self.P = None # likelihood matrix for paired positions
def setupDefaultTree( idx, pname, listCodeList ):
""" 프로젝트가 생성될시 디폴트 트리를 구성합니다 """
# 프로젝트 정보 트리에 등록
treeList = [{ "text": pname, "pos":"000", "pidx": idx, "place": "PROJECT", "ntype": "PAGE", "icon":"ico-project", "gourl": "/project/view2/"+ str(idx), "issystem": True }]
# 기본 리스트 트리에 등록
for nListCode in listCodeList:
alist = gLister[nListCode]
treeList.append({
"text": alist.Title,
"pos": "001",
"pidx": idx,
"place": "PROJECT",
"icon": alist.IconCls,
"ntype": "LIST",
"gourl": "/lister/list2/%s/%s" % (nListCode, idx),
"issystem": True})
Tree.save( treeList )
return
def reset_trees(self):
self.__canvas = CanvasApp(self, 6, 8)
self.__tpa = Tree(PointerAlgorithm())
self.__ms1 = MH(1)
self.__ms2 = MH(2)
fill_tree(self.__tpa, self.__canvas)
self.__ms1.add_gui(self.__canvas.add_circle(0, 0, 0, fill="white", outline="black", width=2, name="MS 1"))
self.__tpa.put_ms_into_node_name(self.__ms1, 7)
self.__ms2.add_gui(self.__canvas.add_circle(0, 0, 0, fill="white", outline="black", width=2, name="MS 2"))
self.__tpa.put_ms_into_node_name(self.__ms2, 18)
def reversible_subtree_func(copy_S, copy_P, parent, child, t_a): #assert len(t_a) == 1 child.edge_length = t_a[0] order = Tree.postorder_cheat_traverse(parent) L_single = g(tlobj.single_model.gtr.R, copy_S, tlobj.log_freq_single, \ order, range(tlobj.ncol), tlobj.nnode, tlobj.ncol, tlobj.nbase) L_paired = g(tlobj.paired_model.gtr.R, copy_P, tlobj.log_freq_paired, \ order, range(tlobj.ncol_p), tlobj.nnode_p, tlobj.ncol_p, tlobj.nbase_p) ans = -(L_single.sum() + L_paired.sum()) return ans
def test(self, specs_file, data_test_file):
trees = tr.load_trees(self.data_folder + data_test_file)
assert specs_file is not None, "Please provide a model to test."
with open(specs_file, 'r') as fid:
_ = pickle.load(fid)
_ = pickle.load(fid)
rntn = RNTN.RNTN(self.output_dim, self.num_words, self.mini_batch_size)
rntn.stack = pickle.load(fid)
print "Testing..."
cost, correct, total = rntn.cost_and_gradients(trees, test=True)
print "\nCost : :%f \nCorrectly labelled : %d/%d \nAccuracy : %f." % (cost, correct, total, correct / float(total))
def fit(self, word_map_file='word_map.bin'):
data_train_file = self.opts.training_file
print '================\nTRAINING\n================'
# If the word map file does not exist, create it.
if not os.path.exists(word_map_file):
tr.build_word_map(data_train_file, word_map_file)
self.word_map_file = word_map_file
# Load trees and set the RNTN.
self.trees = tr.load_trees(self.opts.data_folder + data_train_file, self.word_map_file)
self.num_words = len(tr.load_word_map(self.word_map_file))
self.rntn = RNTN.RNTN(vec_dim=self.opts.vect_dim,
output_dim=self.opts.output_dim,
num_words=self.num_words,
mini_batch_size=self.opts.mini_batch_size)
self.sgd = optimizer.SGD(self.rntn, self.opts.learning_rate, self.opts.mini_batch_size)
for e in range(self.opts.optim_epochs):
# Fit model.
# After the training phase, model specifications
# are in self.rntn.stack.
# --------------------------
start = time.time()
print "Running epoch %d" % e
self.sgd.optimize(self.trees)
end = time.time()
print "Time per epoch : %f" % (end-start)
def test(self):
data_test_file = self.opts.testing_file
print '\n================\nTESTING\n================'
trees = tr.load_trees(self.opts.data_folder + data_test_file)
# Load the test RNTN.
test_rntn = RNTN.RNTN(self.opts.vect_dim,
self.opts.output_dim,
self.num_words,
self.opts.mini_batch_size)
test_rntn.params = self.rntn.params
test_rntn.V, test_rntn.W, test_rntn.b, test_rntn.W_s, test_rntn.b_s = \
self.rntn.V, self.rntn.W, self.rntn.b, self.rntn.W_s, self.rntn.b_s
test_rntn.L = self.rntn.L
cost, correct, total = test_rntn.cost_and_updates(trees, 1, test=True)
print "Cost : %f, Correct : %d/%d, Acc %f %%" % (cost, correct, total, 100 * correct / float(total))
def rearr(t, log_l, rL, rU):
for target in t.postorder_node_iter():
if target.parent_node is None:
break
# first make a deep copy of the tree
t_prime = dendropy.Tree(t)
print "finding subtree rearrangement for {0}".format(target.label)
dest = cello.subtree_rearrangement(t_prime, target, rL, rU)
print "inserting subtree {0} into branch above {1}".format(target.label, dest.label)
try:
# attach the subtree of <target> to branch above <dest>
t_prime, affected = Subtree.move_subtree(t_prime, target.label, dest.label)
except Exception:
print "could not succesfully do the move, forget it! move on!"
continue
cheat_order = Tree.postorder_traverse_with_cheat(t_prime, affected)
# the three local branche we need to optimize are edges of
# target, dest, and parent of target
lst = []
for i, (k, bunch) in enumerate(cheat_order):
for j, (a, t_a) in enumerate(bunch):
if a in (target.label, dest.label, target.parent_node.label):
lst.append((i, j))
print 'list is', lst
log_l_prime = MyMat.optimize_branch_fast(lst, cheat_order, msa, single_model, paired_model, S, P)
if log_l_prime < log_l:
print "subtree rearrangement improved log like from {0}-->{1}, keep!".format(log_l, log_l_prime)
t = t_prime
log_l = log_l_prime
# put in the fastly optimized branch lengths
for i, j in lst:
t.find_node_with_label(cheat_order[i][1][j][0]).edge_length = cheat_order[i][1][j][1]
raw_input("continue?")
return
else:
print "subtree rearrangment made log likelihood worse. Move on"
raw_input("continue")
return
return t, log_l
def p_empty(p):
'''empty :'''
p[0] = Tree()
p[0].value = "empty"
from Tree import *
from Node import *
from numpy import random
from random import randint
from math import log
import numpy as np
c = Counter()
count = []
logn = []
for k in range(1,50):
n = int(5*((1.2)**k))
j = randint(1,n)
N = Tree(c)
logn.append(log(n))
for i in range(1,n+1):
j = randint(1,n)
N.insert(j)
#N.delete(N.root)
avg = N.c.query()/n
N.c.reset()
count.append(avg)
#inorder(N.root)
#print(count)
import matplotlib.pyplot as plt
logn = np.array(logn)
count = np.array(count)
"""
Given a sorted (increasing order) array with unique integer elements, write an algorithm to create a binary search tree with minimal height.
"""
import Tree
def createMBST_a(ary, start, end):
if end < start:
return None
mid = int((start + end) / 2)
n = Tree.Node(ary[mid])
n.left = createMBST_a(ary, start, mid - 1)
n.right = createMBST_a(ary, mid + 1, end)
return n
def createMBST(ary):
return createMBST_a(ary, 0, len(ary) - 1)
ary = [0, 1, 2, 3, 4, 5, 6]
n = createMBST(ary)
Tree.levelorderTraversal(n)
def isBST2(bt):
return isBST2_a(bt, -99999, 99999)
def isBST2_a(bt, min, max):
if bt is None:
return True
if bt.data <= min or bt.data > max:
return False
if not isBST2_a(bt.left, min, bt.data) or not isBST2_a(bt.right, bt.data, max):
return False
return True
bt = Tree.Node(1)
bt.left = Tree.Node(0)
bt.right = Tree.Node(20)
Tree.levelorderTraversal(bt)
print()
print(isBST(bt))
print(isBST2(bt))
bt2 = Tree.Node(10)
bt2.left = bt
Tree.levelorderTraversal(bt2)
print()
print(isBST(bt2)) # wrong
print(isBST2(bt2))
bt2 = Tree.Node(10)
bt2.left = bt
bt2.right = Tree.Node(9)
Tree.levelorderTraversal(bt2)
def visualize(self, filename):
Tree.eteVisualize(self.toEteTree(), filename)
For the purposes of this question, a balanced tree is defined to be a tree such that the heights of the two subtrees of any node never differ by more than one.
"""
import Tree
def isBalanced(bt):
if bt is None:
return True
d = Tree.getHeight(bt.left) - Tree.getHeight(bt.right)
if abs(d) > 1:
return False
else:
return isBalanced(bt.left) and isBalanced(bt.right)
# Balanced tree
bt = Tree.createPerfectTree(3)
Tree.levelorderTraversal(bt)
print()
print(Tree.getHeight(bt))
print(isBalanced(bt))
# Unbalanced tree
bt = Tree.Node(0)
bt.left = Tree.Node(1)
bt.right = Tree.Node(2)
bt2 = Tree.Node(3)
bt2.left = bt
Tree.levelorderTraversal(bt2)
print()
print(Tree.getHeight(bt2))
print(isBalanced(bt2))
def create_DCC_mirror(s, dcc_handle, dirpath, **kwargs):
tr = Tree.get_tree(s,dcc_handle)
Tree.print_tree(s, tr)
docList = Tree.get_flat_tree(tr)
traverse(s, tr, tr['root']['collections'][0], dirpath, **kwargs)
def p_statement_newline(p):
'''simple_statement : newline_opt_comment'''
p[0] = Tree()
p[0].value = "empty"
def main(): s = RandomFSSequence( no_of_shifts=2, min_length=50, max_length=100 ) s.generate() print s.info( "without UGA" ) # a Sequence object #t = BiologicalSequence( s.sequence ) #t = BiologicalSequence( "GCTGGTGGGGTAGCAGGTGTTTCTGTTGACTTGATATTATTTCCTCTGGATACCATTAAAACCAGGCTGCAGAGTCCCCAAGGATTTAGTAAGGCTGGTGGTTTTCATGGAATATATGCTGGCGTTCCTTCTGCTGCTATTGGATCCTTTCCTAATG" ) t = BiologicalSequence( "AAATGACGAACACAGAGGAAAGAAGAGAGGCAACTGCTGAGGTCCCCTAGGCCTTTGAGAAAACGGAGTTGTACCTTTGGCAACATAAGTGCATATCTACAAGAAAGGCGATAATGTAGACACCAAGGGAATGGGTACTGTCCAAAAAGAAATGCCTCACAAATGTCACCATGGCAAAACTAAAAGAGTCTACAAAGTTACCTAGCATGCTGTTGGCATCATTGTAAACAAACAAGTTAAGGGCAAGATTCTTGCCAAGAGAATTAATATGCATATTGGGCATATTAAGCACTCTAAGAGCCAAGATGATTTCCTGAAAGTGTGTGAAGGAAAATAACCAGCATAAAGAGGGAAGCTAAAGAGAAACCTGAAGCTGCAGCCTGTTCCACCCAGAGAAGCACACTTTGTAAGAACCAATGAAAAGGAGCCTGAGCTGCTGGAGTCTATTAACTGAATTCATGGT" ) #t = RandomSequence( 100 ) #t.generate() # t.stops.append( "UGA" ) print print t.info() for i in xrange( 3 ): print t.colour_frame( i, sep="" ) print " |"*(( s.length )//10 ) t.get_stop_sequence() print "The raw stop sequence (%s)..." % len( t.stop_sequence ) print t.stop_sequence print # now to create paths p = Paths( t.stop_sequence ) print "The sanitised stop sequence (%d)..." % len( p.unique_frame_sequence ) print p.unique_frame_sequence print print "Create the branches from the stop sequence..." p.create_branches() print print "Create a tree..." T = Tree() print "View and graft the branches..." for B in p.branches: #print B T.graft( B ) print T print "Build the paths..." paths = T.get_paths( simplify=True ) print paths print for frame in xrange( 3 ): print "Frameshift sequences for frame %d:" % frame for j in T.get_frame_paths( frame ): print len( j ), " : ", j print """ frameshifted_sequence, fragments = t.frameshift_from_path( all_paths[0] ) q = BiologicalSequence( s.sequence ) print s.info() for i in xrange( 3 ): print q.colour_frame( i, sep="" ) print " |"*(( s.length )//10 ) print print " ".join( fragments ) print print t.path print t.colour_frameshifted_sequence( sep="" ) print " |"*(( s.length )//10 ) """ for i in xrange( 3 ): all_paths = T.get_frame_paths( i ) for a in all_paths: frameshifted_sequence, fragments, frameshift_signals = t.frameshift_from_path( a ) print t.path print t.colour_frameshifted_sequence( sep="" ) print " ".join( fragments ) print " ".join( frameshift_signals ) print
def checkPerms(target, permissions):
# Login to DCC
s = DCC.login(CF.dcc_url + CF.dcc_login)
if 'Collection' in target:
tr = Tree.get_tree(s,target)
Tree.print_tree(s, tr)
docList = Tree.get_flat_tree(tr)
else:
docList = [target]
printCheckCriteria(target, permissions)
passList = []
failList = []
for doc in docList:
checkFlag = True
if 'Document' in doc:
fd = DCC.prop_get(s, doc, InfoSet = 'DocBasic')
fd['permissions'] = DCC.prop_get(s, doc, InfoSet = 'Perms', Depth = '0')
print("\n\n*** Document Entry", fd['handle'], "***")
print("DCC Name: \"",fd['title'],"\"",sep="")
print("TMT Document Number: ", fd['tmtnum'])
print("https://docushare.tmt.org/docushare/dsweb/ServicesLib/" + fd['handle'] + "/view")
elif 'Collection' in doc:
fd = DCC.prop_get(s, doc, InfoSet = 'CollData')
fd['permissions'] = DCC.prop_get(s, doc, InfoSet = 'Perms', Depth = '0')
print("\n\n*** Collection Entry", fd['handle'], "***")
print("https://docushare.tmt.org/docushare/dsweb/ServicesLib/" + fd['handle'] + "/view")
else:
checkFlag = False
print("\nNot checking permissions on object that is not a Collection or Document):",doc)
if checkFlag:
OkayPerms = []
for perm in sorted(fd["permissions"]["perms"], key = lambda x: x["handle"]):
# Go through each set and create a dictionary of entries that have perms okay
for sets in permissions:
for item,plist in sets.items():
if perm["handle"] == item:
if printCheckPerms(perm,plist) == True:
OkayPerms.append(item)
permFlag = False
for sets in permissions:
testFlag = True
for item in sets:
if not item in OkayPerms:
testFlag = False
if testFlag == True:
permFlag = True
if permFlag == True:
print("*** PERMISSIONS MEET CRITERIA ***")
passList.append(doc)
else:
print("!!! PERMISSIONS DO NOT MEET CRITERIA !!!")
failList.append(doc)
return([passList,failList])
default=0)
parser.add_argument('--maxChildren', '-M',
help="The maximum number of children by nodes",
type=int,
default=8)
parser.add_argument('--alpha', '-a',
help="The 'a' parameter of the Weibull distribution "
"used to create the task length.",
type=float,
default=1)
parser.add_argument('--scale', '-s',
help="The mean time of each task.",
type = float,
default=1)
parser.add_argument('--filename', '-f',
help="The filename to save the tree.",
default="tree.txt")
args = parser.parse_args()
if args.minChildren > args.maxChildren:
args.maxChildren = args.minChildren + 1
tree = Tree.Tree(args.height,
args.minChildren,
args.maxChildren,
args.alpha,
*Tree.calibrate(args.scale))
Tree.exportTree(tree, args.filename)
print("Generated :\n{0}".format(tree))
tree = None,
branch_scale = 0,
height_scale = 0,
)
(options, args) = Experiment.Start( parser, add_pipe_options = True )
if options.filename_tree:
tree_lines = open(options.filename_tree, "r").readlines()
elif options.tree:
tree_lines = options.tree
else:
raise "please supply a species tree."
nexus = TreeTools.Newick2Nexus( tree_lines )
Tree.updateNexus( nexus )
tree = nexus.trees[0]
if options.loglevel >= 2:
tree.display()
plot = SVGTree( tree )
plot.setBranchScale( options.branch_scale )
plot.setHeightScale( options.height_scale )
if options.colour_by_species:
rx = re.compile(options.species_regex)
extract_species = lambda x: rx.search( x ).groups()[0]
plot.setDecoratorExternalNodes( NodeDecoratorBySpecies( tree, extract_species= extract_species ) )
