From a755ce54116e990c0f8181c2909bd556319de1b8 Mon Sep 17 00:00:00 2001 From: Chenjy0212 <1026224216@qq.com> Date: Mon, 26 Sep 2022 15:30:35 +0800 Subject: [PATCH] main_lab --- mdelta/Pvalue.py | 0 mdelta/ReadFile.py | 12 +- mdelta/__init__.py | 77 ++-- mdelta/__version__.py | 5 - mdelta/auxi.py | 0 mdelta/mDELTA.py | 914 ++++++++++++++++++++---------------------- mdelta/mymath.py | 0 mdelta/mytest.py | 0 8 files changed, 495 insertions(+), 513 deletions(-) mode change 100644 => 100755 mdelta/Pvalue.py mode change 100644 => 100755 mdelta/ReadFile.py mode change 100644 => 100755 mdelta/__init__.py delete mode 100644 mdelta/__version__.py mode change 100644 => 100755 mdelta/auxi.py mode change 100644 => 100755 mdelta/mDELTA.py mode change 100644 => 100755 mdelta/mymath.py mode change 100644 => 100755 mdelta/mytest.py diff --git a/mdelta/Pvalue.py b/mdelta/Pvalue.py old mode 100644 new mode 100755 diff --git a/mdelta/ReadFile.py b/mdelta/ReadFile.py old mode 100644 new mode 100755 index 480fef5..64d298f --- a/mdelta/ReadFile.py +++ b/mdelta/ReadFile.py @@ -30,19 +30,21 @@ def ReadTreeSeq(TreeSeqFilePath): #print(content) return content.replace(';', '') -def Scoredict(lllleaf, llllleaf, mv:float): +def Scoredict(lllleaf, llllleaf, mav:float, miv:float): #如果是自动生成的话 #可以用到笛卡尔积 score_dict = {} - #for i in itertools.product(set(lllleaf), set(llllleaf)): - # score_dict[i[0].nodeobj+'_'+i[1].nodeobj] = float(random.randint(-2,2)) + for i in itertools.product(set(lllleaf), set(llllleaf)): + score_dict[i[0].nodeobj+'_'+i[1].nodeobj] = float(miv) #score_dict[i[0].nodeobj+'_'+i[1].nodeobj] = random.random()/10 #print(score_dict) #或者用到相同节点才匹配 #score_dict = {} - for i in lllleaf + llllleaf: - score_dict[i.nodeobj+'_'+i.nodeobj] = mv + for i in lllleaf: + score_dict[i.nodeobj+'_'+i.nodeobj] = float(mav) + for i in llllleaf: + score_dict[i.nodeobj+'_'+i.nodeobj] = float(mav) return score_dict def reverseScore(Score, matchScore:float): diff --git a/mdelta/__init__.py b/mdelta/__init__.py old mode 100644 new mode 100755 index 5050e76..cc85f1d --- a/mdelta/__init__.py +++ b/mdelta/__init__.py @@ -474,25 +474,30 @@ def scoremat(TreeSeqFile:str, llll_label = [i.label for i in llll[0]], merge = merge,) - def changemat(rac, tracemat, tracemat_value, mat, list_tmp1, list_tmp2): - for i in tracemat_value[tuple(rac)]: - if isinstance(i[0],int) and isinstance(i[1],int): - list_tmp1.append(tracemat.index[i[0]]) - list_tmp2.append(tracemat.columns[i[1]]) - elif not isinstance(i[0],int) and not isinstance(i[1],int): - list_tmp1.append(tracemat.index[rac[0]]) - list_tmp2.append(tracemat.columns[rac[1]]) - return - #print(i[0],i[0]) - for i in tracemat_value[tuple(rac)]: - if isinstance(i[0],int) and isinstance(i[1],int): - #print(i[0],root1.leaf_count(), i[1],root2.leaf_count()) - if i == [] or (i[0] 0: - if len(scorelist[-1]['Root1_match']) == 0: - percent = 0 - else: - percent = (len(list(set(list_tmp1) - set(scorelist[-1]['Root1_match'])) + list(set(scorelist[-1]['Root1_match']) - set(list_tmp1))) / len(scorelist[-1]['Root1_match']))*100. - if round(percent) < diff: - #print(round(percent)) + if jjj > 0 and diff > 0: + for i in range(len(scorelist)): + percent = min(percent, len(set(scorelist[i]['used']) - set(ttused)) / len(scorelist[i]['used'])*100.) + + while percent < float(diff): maxscore = np.max(mat_tmp) + if (maxscore + 99999.) < 0.01: + break del_i_index = np.where(mat_tmp==np.max(mat_tmp))[0][0] del_j_index = np.where(mat_tmp==np.max(mat_tmp))[1][0] list_tmp1 = [] list_tmp2 = [] mat_tmp[del_i_index,del_j_index] = -99999. - changemat([del_i_index,del_j_index],ttrace, trace_value,mat_tmp, list_tmp1, list_tmp2) + ttused = [str([del_i_index, del_j_index])] + changemat([del_i_index,del_j_index],ttrace, trace_value,mat_tmp, list_tmp1, list_tmp2, ttused) tree_tmp1 = [] tree_tmp2 = [] @@ -644,6 +654,9 @@ def where_prune(match_list:list, leaves_list:list): tree_tmp4 = [] mat_tmp3[del_i_index,del_j_index] = -99999. getmatchtree([del_i_index,del_j_index],lllnode_label, llllnode_label, trace_value,mat_tmp3, tree_tmp3, tree_tmp4) + percent = 100 + for i in range(len(scorelist)): + percent = min(percent, len(set(scorelist[i]['used']) - set(ttused)) / len(scorelist[i]['used'])*100.) tree_tmp1.append(';') tree_tmp2.append(';') @@ -668,7 +681,9 @@ def where_prune(match_list:list, leaves_list:list): 'Root1_prune':where_prune(list_tmp1, list(map(lambda x:x.label,lllnode[del_i_index].leaves([])))), 'Root2_prune':where_prune(list_tmp2, list(map(lambda x:x.label,llllnode[del_j_index].leaves([])))), 'row':del_i_index, - 'col':del_j_index}) + 'col':del_j_index, + 'used':ttused + }) return({'matrix':mmatrix, 'tree1_leaves_nodename': oroot1_label2celltype[1], diff --git a/mdelta/__version__.py b/mdelta/__version__.py deleted file mode 100644 index 7d29ee5..0000000 --- a/mdelta/__version__.py +++ /dev/null @@ -1,5 +0,0 @@ -#EeWhile - -VERSION = (1, 0, 0) - -__version__ = '.'.join(map(str, VERSION)) diff --git a/mdelta/auxi.py b/mdelta/auxi.py old mode 100644 new mode 100755 diff --git a/mdelta/mDELTA.py b/mdelta/mDELTA.py old mode 100644 new mode 100755 index 854ad32..0e39c85 --- a/mdelta/mDELTA.py +++ b/mdelta/mDELTA.py @@ -1,5 +1,6 @@ # -*- coding: utf-8 -*- - +#20220920 +import os from ReadFile import * from mytest import * from mymath import * @@ -11,45 +12,30 @@ from multiprocessing import * import multiprocessing as mp import psutil -import os - -parser = argparse.ArgumentParser(prog='mDELTA', description=' Multifuricating Developmental cEll Lineage Tree Alignment(mDELTA) ', - epilog='More details on https://github.com/Chenjy0212/modelta') -parser.add_argument('TreeSeqFile', type=str, - help='[path/filename] A text file storing cell lineage tree #1 in newick format. Tips can be labeled by name or cell type. Branch lengths should be removed.') -parser.add_argument('TreeSeqFile2', type=str, - help='[path/filename] A text file storing cell lineage tree #2 in newick format. Tips can be labeled by name or cell type. Branch lengths should be removed.') -parser.add_argument('-nt', '--Name2TypeFile', type=str, default='', - help='[path/filename] List of correspondance between tip name and cell type for cell lineage tree #1.') -parser.add_argument('-nt2', '--Name2TypeFile2', type=str, default='', - help='[path/filename] List of correspondance between tip name and cell type for cell lineage tree #2.') -parser.add_argument('-sd', '--ScoreDictFile', type=str, default='', help='[path/filename] A comma-delimited text file used to determine similarity scores between cells. If there are exactly three columns, they will be interpreted as (1) the cell (name or type) in Tree #1, (2) the cell in Tree #2, and (3) the similarity score. If otherwise, the first column will be interpreted as the cell (name or type) and the remaining columns as features of the cell (e.g. expression of a gene). The similarity scores will be estimated between all pairs of cells based on the Euclidean distance calculated using all the features. Overrides `-ma` and `-mi`.') -parser.add_argument('-t', '--top', type=int, default=0, - help='[int > 0] Performs local (instead of global) alignment, and output the top NUM local alignments with the highest score (e.g. `-t 10`). In the case of global alignment, this parameter should be omitted.') -parser.add_argument('-ma', '--mav', type=float, - default=2., help=' [float] Default=2.') -parser.add_argument('-mi', '--miv', type=float, default=-1., - help=' [float] Default=-1. Shorthand for a simple matching score scheme, where the matching score between a pair of the same cell types is MAV and all other pairs are MIV. (e.g. `-ma 2 -mi -2`). Overridden by `-sd`.') -parser.add_argument('-p', '--pv', type=float, default=-1., - help=' [float] The score for pruning a tip of the tree (e.g. `-p -2`). Default to -1.') -parser.add_argument('-T', '--Tqdm', type=int, default=1, - help=' [0(off) or 1(on)] Toggle for the jupyter notebook environment.') -parser.add_argument('-n', '--notebook', type=int, default=0, - help='[0(off) or 1(on)] Toggle for the jupyter notebook environment.') -parser.add_argument('-P', '--PERM', type=int, default=0, - help='[int > 0] Toggle for the statistical significance. For each observed alignment, the aligned trees will be permuted PERM times to generate a null distribution of alignment scores, with which a P value can be calculated for the observed alignment score.') -parser.add_argument('-a', '--Alg', type=str, default='KM', - help='[KM / GA] Use Kuhn-Munkres or Greedy Algorithm to find the optimal alignment score.') -parser.add_argument('-c', '--CPUs', type=int, default=50, - help='[int > 0] Number of threads for multi-processing. Default to 50., it can reach the maximum number of local CPU cores - 1.') -parser.add_argument('-o', '--output', type=str, default='KM', - help='[path/filename] Output filename') -parser.add_argument('-x', '--diff', type=int, default=0, - help=' [int > 0] Alignment must consist of a minimal of DIFF% aligned cell pairs that are different from previous(better) local alignments in order to be considered as another new alignment (e.g. `-x 20` means 20 persent).') -parser.add_argument('-mg', '--merge', type=float, default=10, - help='[float] This is the scaling factor for calculating the score of merging an internal node (e.g. -mg -1), which is multiplied by the number of tips of the internal node to be merged. Default to 0.') - -args = parser.parse_args() # 开始解析参数 --对于可选参数来说 +import time +time_start = time.time() # 记录开始时间 + + +parser = argparse.ArgumentParser(prog='mDELTA', description = ' Multifuricating Developmental cEll Lineage Tree Alignment(mDELTA) ',epilog = 'More details on https://github.com/Chenjy0212/modelta') +parser.add_argument('TreeSeqFile',type=str, help='[path/filename] A text file storing cell lineage tree #1 in newick format. Tips can be labeled by name or cell type. Branch lengths should be removed.') +parser.add_argument('TreeSeqFile2',type=str, help='[path/filename] A text file storing cell lineage tree #2 in newick format. Tips can be labeled by name or cell type. Branch lengths should be removed.') +parser.add_argument('-nt','--Name2TypeFile',type=str, default='', help='[path/filename] List of correspondance between tip name and cell type for cell lineage tree #1.') +parser.add_argument('-nt2','--Name2TypeFile2',type=str, default='', help='[path/filename] List of correspondance between tip name and cell type for cell lineage tree #2.') +parser.add_argument('-sd','--ScoreDictFile',type=str, default='', help='[path/filename] A comma-delimited text file used to determine similarity scores between cells. If there are exactly three columns, they will be interpreted as (1) the cell (name or type) in Tree #1, (2) the cell in Tree #2, and (3) the similarity score. If otherwise, the first column will be interpreted as the cell (name or type) and the remaining columns as features of the cell (e.g. expression of a gene). The similarity scores will be estimated between all pairs of cells based on the Euclidean distance calculated using all the features. Overrides `-ma` and `-mi`.') +parser.add_argument('-t','--top',type=int, default=0, help='[int > 0] Performs local (instead of global) alignment, and output the top NUM local alignments with the highest score (e.g. `-t 10`). In the case of global alignment, this parameter should be omitted.') +parser.add_argument('-ma','--mav',type=float, default=2., help=' [float] Default=2.') +parser.add_argument('-mi','--miv',type=float, default=-1., help=' [float] Default=-1. Shorthand for a simple matching score scheme, where the matching score between a pair of the same cell types is MAV and all other pairs are MIV. (e.g. `-ma 2 -mi -2`). Overridden by `-sd`.') +parser.add_argument('-p','--pv',type=float, default=-1., help=' [float] The score for pruning a tip of the tree (e.g. `-p -2`). Default to -1.') +parser.add_argument('-T','--Tqdm',type=int, default=1, help=' [0(off) or 1(on)] Toggle for the jupyter notebook environment.') +parser.add_argument('-n','--notebook',type=int, default=0, help='[0(off) or 1(on)] Toggle for the jupyter notebook environment.') +parser.add_argument('-P','--PERM',type=int, default=0, help='[int > 0] Toggle for the statistical significance. For each observed alignment, the aligned trees will be permuted PERM times to generate a null distribution of alignment scores, with which a P value can be calculated for the observed alignment score.') +parser.add_argument('-a','--Alg',type=str, default='KM', help='[KM / GA] Use Kuhn-Munkres or Greedy Algorithm to find the optimal alignment score.') +parser.add_argument('-c','--CPUs',type=int, default=50, help='[int > 0] Number of threads for multi-processing. Default to 50., it can reach the maximum number of local CPU cores - 1.') +parser.add_argument('-o','--output',type=str, default='KM', help='[path/filename] Output filename') +parser.add_argument('-x','--diff',type=int, default=0, help='[int > 0] Alignment must consist of a minimal of DIFF% aligned cell pairs that are different from previous(better) local alignments in order to be considered as another new alignment (e.g. `-x 20` means 20%).') +parser.add_argument('-mg','--merge',type=float, default=10, help='[float] This is the scaling factor for calculating the score of merging an internal node (e.g. -mg -1), which is multiplied by the number of tips of the internal node to be merged. Default to 0.') + +args = parser.parse_args() #开始解析参数 --对于可选参数来说 TreeSeqFile = args.TreeSeqFile TreeSeqFile2 = args.TreeSeqFile2 @@ -68,57 +54,51 @@ diff = args.diff merge = args.merge - class MultiTree: - def __init__(self, nodeobj: str, level: 'int' = 0, label: str = "root"): + def __init__(self, nodeobj:str, level:'int'=0, label:str="root"): self.nodeobj = nodeobj if nodeobj[-1] != ';' else nodeobj[:-1] self.left = None self.right = None self.level = level self.label = label # 前序遍历,获取按照层级排序的全节点序列对 [node:level] - - def nodes(self, ll={}): - lll = ll + def nodes(self,ll={}): + lll= ll if self.nodeobj is not None: - lll[self] = self.level + lll[self]=self.level #print(self.nodeobj,' Level:',self.level,' Label:',self.label) if self.left is not None: self.left.nodes(lll) if self.right is not None: self.right.nodes(lll) - - lll = sorted(lll.items(), key=lambda item: item[1]) + + lll = sorted(lll.items(), key=lambda item:item[1]) llist = [[] for i in range(lll[-1][1]+1)] - for i in lll: + for i in lll: llist[i[1]].append(i[0]) return llist # 后序遍历_得到nodeobj:level对 - def postorder(self): if self.left is not None: self.left.postorder() if self.right is not None: self.right.postorder() if self.nodeobj is not None: - print(self.nodeobj, ' Level:', self.level, ' Label:', self.label) + print(self.nodeobj,' Level:',self.level,' Label:',self.label) # 前序遍历_得到nodeobj:level对 - def preorder(self): if self.nodeobj is not None: - print(self.nodeobj, ' Level:', self.level, ' Label:', self.label) + print(self.nodeobj,' Level:',self.level,' Label:',self.label) if self.left is not None: self.left.preorder() if self.right is not None: self.right.preorder() # 层序遍历 - def levelorder(self): # 返回某个节点的左孩子 def LChild_Of_Node(node): return node.left if node.left is not None else None # 返回某个节点的右孩子 - def RChild_Of_Node(node): return node.right if node.right is not None else None # 层序遍历列表 @@ -149,7 +129,6 @@ def RChild_Of_Node(node): level_order[i][index] = level_order[i][index].nodeobj return level_order # 二叉树的高度 - def height(self): # 空的树高度为0, 只有root节点的树高度为1 if self.nodeobj is None: @@ -163,8 +142,7 @@ def height(self): else: return 1 + max(self.left.height(), self.right.height()) # 原来的多叉树的叶子节点 - - def leaves(self, ll=[], flag=1): # 意味 0 这个是叶子节点 + def leaves(self,ll=[],flag = 1): #意味 0 这个是叶子节点 if self is None: return None if flag == 1: @@ -173,17 +151,17 @@ def leaves(self, ll=[], flag=1): # 意味 0 这个是叶子节点 return ll else: self = self.left - + if self.left is not None: - self.left.leaves(ll, flag=0) + self.left.leaves(ll,flag = 0) if self.nodeobj is not None: if self.left is None: ll.append(self) if self.right is not None: - self.right.leaves(ll, flag=0) + self.right.leaves(ll,flag = 0) return ll - - def leaves_nodeobj(self, ll=[], flag=1): # 意味 0 这个是叶子节点 + + def leaves_nodeobj(self,ll=[],flag = 1): #意味 0 这个是叶子节点 if self is None: return None if flag == 1: @@ -192,17 +170,17 @@ def leaves_nodeobj(self, ll=[], flag=1): # 意味 0 这个是叶子节点 return ll else: self = self.left - + if self.left is not None: - self.left.leaves_nodeobj(ll, flag=0) + self.left.leaves_nodeobj(ll,flag = 0) if self.nodeobj is not None: if self.left is None: ll.append(self.nodeobj) if self.right is not None: - self.right.leaves_nodeobj(ll, flag=0) + self.right.leaves_nodeobj(ll,flag = 0) return ll - - def leaves_label(self, ll=[], flag=1): # 意味 0 这个是叶子节点 + + def leaves_label(self,ll=[],flag = 1): #意味 0 这个是叶子节点 if self is None: return None if flag == 1: @@ -211,72 +189,67 @@ def leaves_label(self, ll=[], flag=1): # 意味 0 这个是叶子节点 return ll else: self = self.left - + if self.left is not None: - self.left.leaves_label(ll, flag=0) + self.left.leaves_label(ll,flag = 0) if self.nodeobj is not None: if self.left is None: ll.append(self.label) if self.right is not None: - self.right.leaves_label(ll, flag=0) + self.right.leaves_label(ll,flag = 0) return ll - - # 生成树 + + #生成树 def CreatTree(self): - if(self.nodeobj[0] == '('): # 存在括号意味着还没达到叶子结点 + if(self.nodeobj[0] == '('): #存在括号意味着还没达到叶子结点 node_list = [] - brackets_num = 0 # 括号个数 - node_num = 0 # 节点序号 + brackets_num = 0 #括号个数 + node_num = 0 #节点序号 node_tmp = '' for i in self.nodeobj[1:-1]: if i == '(': - brackets_num += 1 + brackets_num +=1 node_tmp += i elif i == ')': - brackets_num -= 1 + brackets_num -=1 node_tmp += i elif i == ',' and brackets_num == 0: node_list.append(node_tmp) - node_num += 1 + node_num +=1 node_tmp = '' else: node_tmp += i - node_list.append(node_tmp) - # print(node_list) #查看拆分后的节点序列 + node_list.append(node_tmp) + #print(node_list) #查看拆分后的节点序列 self_tmp = self - for index, item in enumerate(node_list): + for index,item in enumerate(node_list): if index == 0: - # 第一个子节点都是父节点的左节点 - # 后续子节点就是上一个子节点的右节点 - # 赋予label - label = str( - index) if self.label == 'root' else self_tmp.label+'_'+str(index) - self.left = MultiTree(item, label=label) + #第一个子节点都是父节点的左节点 + #后续子节点就是上一个子节点的右节点 + #赋予label + label = str(index) if self.label == 'root' else self_tmp.label+'_'+str(index) + self.left = MultiTree(item,label=label) self = self.left self.CreatTree() else: - label = str( - index) if not '_' in self.label else self_tmp.label+'_'+str(index) - self.right = MultiTree(item, label=label) + label = str(index) if not '_' in self.label else self_tmp.label+'_'+str(index) + self.right = MultiTree(item,label=label) self = self.right self.CreatTree() - # 赋层左子树后序遍历找出最大层级d - + #赋层左子树后序遍历找出最大层级d def Posorder_Max_Level(self): - level = self.level + level = self.level; while self.right: - # 有右子树就是有兄弟结点 + #有右子树就是有兄弟结点 level = max(level, self.right.level) self = self.right return level - # 赋予层级level - + #赋予层级level def Level(self): - if self.left == None: # 叶子节点 + if self.left == None: #叶子节点 self.level = 0 else: self.level = self.left.Posorder_Max_Level()+1 - def Postorder_Level(self): if self.left is not None: self.left.Postorder_Level() @@ -284,9 +257,8 @@ def Postorder_Level(self): self.right.Postorder_Level() if self.nodeobj is not None: self.Level() - # 叶子节点个数,需要的是节点下的左节点才正确 - - def leaf_count(self, flag=1): + #叶子节点个数,需要的是节点下的左节点才正确 + def leaf_count(self, flag = 1): if self is None: return 0 if flag == 1: @@ -297,14 +269,14 @@ def leaf_count(self, flag=1): if self.left is None and self.right is None: return 1 elif self.right is not None and self.left is not None: - return self.left.leaf_count(flag=0) + self.right.leaf_count(flag=0) + return self.left.leaf_count(flag = 0) + self.right.leaf_count(flag = 0) elif self.left is None and self.right is not None: - return 1 + self.right.leaf_count(flag=0) + return 1 + self.right.leaf_count(flag = 0) elif self.right is None and self.left is not None: - return self.left.leaf_count(flag=0) - - # 总节点个数 + return self.left.leaf_count(flag = 0) + + #总节点个数 def node_count(self): if self is None: return 0 @@ -316,10 +288,9 @@ def node_count(self): return 1 + self.right.node_count() elif self.right is None and self.left is not None: return 1 + self.left.node_count() - # else: - # return self.left.node_count()+ self.right.node_count() + 1 - # 获取当前节点一级子节点个数 - + #else: + #return self.left.node_count()+ self.right.node_count() + 1 + #获取当前节点一级子节点个数 def son_count(self): self_tmp = self son_num = 0 @@ -333,9 +304,8 @@ def son_count(self): while self_tmp.right: self_tmp = self_tmp.right son_num += 1 - return son_num - # 获取当前节点一级子节点self - + return son_num + #获取当前节点一级子节点self def son(self): self_tmp = self son_tmp = [] @@ -352,8 +322,7 @@ def son(self): son_tmp.append(self_tmp) return son_tmp # 中序遍历，得到生成树数据 - - def inorder(self, data=[]): + def inorder(self,data=[]): if self.left is not None: self.left.inorder(data) if self.nodeobj is not None: @@ -362,7 +331,6 @@ def inorder(self, data=[]): self.right.inorder(data) return data - def label_leaves_list_to_tree(label_list, tree_str): str_tmp = '' flag = 0 @@ -377,40 +345,41 @@ def label_leaves_list_to_tree(label_list, tree_str): else: str_tmp += label_list[flag] str_tmp += ',' - flag += 1 + flag +=1 elif i == ')': if str_tmp[-1] == ')': str_tmp += i else: str_tmp += label_list[flag] str_tmp += i - flag += 1 + flag +=1 return str_tmp - - -def scoremat(TreeSeqFile: str, - TreeSeqFile2: str, - Name2TypeFile: str = '', - Name2TypeFile2: str = '', - ScoreDictFile: str = '', - top: int = 0, - pv: float = -1., - mav: float = 2., - miv: float = -1., - Alg: str = 'KM', - Tqdm: int = 1, - notebook: int = 0, - diff: int = 0, - merge: float = 10.): + + + +def scoremat(TreeSeqFile:str, + TreeSeqFile2:str, + Name2TypeFile:str = '', + Name2TypeFile2:str = '', + ScoreDictFile:str = '', + top:int = 0, + pv:float = -1., + mav:float = 2., + miv:float = -1., + Alg:str = 'KM', + Tqdm:int = 1, + notebook:int = 0, + diff:int = 0, + merge:float = 10.): if Name2TypeFile != '': - TreeSeqType = ReadTreeSeq_Name2Type(TreeSeqFile, Name2TypeFile) + TreeSeqType = ReadTreeSeq_Name2Type(TreeSeqFile,Name2TypeFile) TreeSeqOri = ReadTreeSeq(TreeSeqFile) else: TreeSeqType = ReadTreeSeq(TreeSeqFile) TreeSeqOri = ReadTreeSeq(TreeSeqFile) if Name2TypeFile2 != '': - TreeSeqType2 = ReadTreeSeq_Name2Type(TreeSeqFile2, Name2TypeFile2) + TreeSeqType2 = ReadTreeSeq_Name2Type(TreeSeqFile2,Name2TypeFile2) TreeSeqOri2 = ReadTreeSeq(TreeSeqFile2) else: TreeSeqType2 = ReadTreeSeq(TreeSeqFile2) @@ -423,20 +392,20 @@ def scoremat(TreeSeqFile: str, root1.CreatTree() root1.Postorder_Level() - lll = root1.nodes({}) # 二维表示 - # for i in lll[0]: - # print(i.label) + lll = root1.nodes({}) #二维表示 + #for i in lll[0]: + #print(i.label) lllnode = [j for i in lll for j in i] lllnode_obj = [j.nodeobj for i in lll for j in i] lllnode_label = [j.label for i in lll for j in i] - # print(lllnode_obj) - + #print(lllnode_obj) + # get root1 leaves' new label to celltype infos root1_label2celltype = leafLable_to_celltype_info(lllnode) oroot1.CreatTree() oroot1.Postorder_Level() - olll = oroot1.nodes({}) # 二维表示 + olll = oroot1.nodes({}) #二维表示 olllnode = [j for i in olll for j in i] # get orignal root1 leaves' new label to celltype infos oroot1_label2celltype = leafLable_to_celltype_info(olllnode) @@ -445,23 +414,23 @@ def scoremat(TreeSeqFile: str, for i in lll: lllloop.append(len(i)) llldict = {} - for index, iter in enumerate(lllnode): + for index,iter in enumerate(lllnode): llldict[index] = [lllnode.index(i) for i in iter.son()] root2.CreatTree() root2.Postorder_Level() - llll = root2.nodes({}) # 二维表示 + llll = root2.nodes({}) #二维表示 llllnode = [j for i in llll for j in i] llllnode_obj = [j.nodeobj for i in llll for j in i] llllnode_label = [j.label for i in llll for j in i] - # print(llllnode_obj) - + #print(llllnode_obj) + # get root2 leaves' new label to celltype infos root2_label2celltype = leafLable_to_celltype_info(llllnode) oroot2.CreatTree() oroot2.Postorder_Level() - ollll = oroot2.nodes({}) # 二维表示 + ollll = oroot2.nodes({}) #二维表示 ollllnode = [j for i in ollll for j in i] # get orignal root2 leaves' new label to celltype infos oroot2_label2celltype = leafLable_to_celltype_info(ollllnode) @@ -470,32 +439,31 @@ def scoremat(TreeSeqFile: str, for i in llll: llllloop.append(len(i)) lllldict = {} - for index, iter in enumerate(llllnode): + for index,iter in enumerate(llllnode): lllldict[index] = [llllnode.index(i) for i in iter.son()] - if ScoreDictFile == '': - score_dict = Scoredict(root1.leaves([]), root2.leaves([]), mav, miv) - # print(root2.leaves([])) + if ScoreDictFile == '': + score_dict = Scoredict(root1.leaves([]),root2.leaves([]), mav, miv) + #print(root2.leaves([])) else: - score_dict = QuantitativeScoreFile(root1.leaves( - []), root2.leaves([]), mav, miv, ScoreDictFile) + score_dict = QuantitativeScoreFile(root1.leaves([]),root2.leaves([]),mav, miv, ScoreDictFile) - # print(root1.left.leaves_nodeobj([]),root2.left.leaves_nodeobj([])) - # print(root1.left.leaf_count(),root2.left.leaf_count()) - # print(root1.leaves_nodeobj([]),root2.leaves_nodeobj([])) - # print(score_dict) + #print(root1.left.leaves_nodeobj([]),root2.left.leaves_nodeobj([])) + #print(root1.left.leaf_count(),root2.left.leaf_count()) + #print(root1.leaves_nodeobj([]),root2.leaves_nodeobj([])) + #print(score_dict) mmatrix = pd.DataFrame([[0.0 for i in range(len(llllnode))] for j in range(len(lllnode))], - index=[i.label for i in lllnode], - columns=[j.label for j in llllnode]) + index=[i.label for i in lllnode], + columns=[j.label for j in llllnode]) - mmatrix.index.name = 'Root1' + mmatrix.index.name = 'Root1' mmatrix.columns.name = 'Root2' matrix_values = mmatrix.values ttrace = pd.DataFrame([[[] for i in range(len(llllnode))] for j in range(len(lllnode))], - index=[i.label for i in lllnode], - columns=[j.label for j in llllnode]) - ttrace.index.name = 'Root1' + index=[i.label for i in lllnode], + columns=[j.label for j in llllnode]) + ttrace.index.name = 'Root1' ttrace.columns.name = 'Root2' trace_value = ttrace.values if Tqdm == 1: @@ -503,112 +471,116 @@ def scoremat(TreeSeqFile: str, from tqdm.notebook import tqdm else: from tqdm import tqdm - with tqdm(total=(root1.node_count()*root2.node_count()), desc='Matrix Node') as pbar: - # 执行循有规律就是(0,0)(0,1)(1,0)(1,1)(0,2)(2,0)(1,2)(2,1)(2,2)(0,3)(3,0)...(n,m) - for loop_index in loopindex(root1.level+1, root2.level+1): - # print(loop_index) + with tqdm(total=(root1.node_count()*root2.node_count()),desc='Matrix Node') as pbar: + #执行循有规律就是(0,0)(0,1)(1,0)(1,1)(0,2)(2,0)(1,2)(2,1)(2,2)(0,3)(3,0)...(n,m) + for loop_index in loopindex(root1.level+1,root2.level+1): + #print(loop_index) for i in range(lllloop[loop_index[0]]): - for j in range(llllloop[loop_index[1]]): + for j in range(llllloop[loop_index[1]]): i_index = 0 j_index = 0 for i_tmp in range(loop_index[0]): i_index += lllloop[i_tmp] - i_index += i + i_index+=i for j_tmp in range(loop_index[1]): j_index += llllloop[j_tmp] - j_index += j - matrix_tmp = matrix_values[llldict[i_index], :] - matrix_tmp = matrix_tmp[:, lllldict[j_index]] + j_index+=j + matrix_tmp = matrix_values[llldict[i_index],:] + matrix_tmp = matrix_tmp[:,lllldict[j_index]] matrix_values[i_index][j_index] = GetMaxScore(trace=trace_value, - root1=lll[loop_index[0]][i], - root2=llll[loop_index[1]][j], - allmatrix=matrix_values, - root1_index=i_index, - root2_index=j_index, - local_matrix=matrix_tmp, - local_matrix_root1_index=llldict[i_index], - local_matrix_root2_index=lllldict[j_index], - dict_score=score_dict, - Algorithm=Alg, - prune=pv, - lll_label=[ - i.label for i in lll[0]], - llll_label=[ - i.label for i in llll[0]], - merge=merge,) + root1=lll[loop_index[0]][i], + root2=llll[loop_index[1]][j], + allmatrix = matrix_values, + root1_index=i_index, + root2_index=j_index, + local_matrix=matrix_tmp, + local_matrix_root1_index = llldict[i_index], + local_matrix_root2_index = lllldict[j_index], + dict_score=score_dict, + Algorithm=Alg, + prune=pv, + lll_label = [i.label for i in lll[0]], + llll_label = [i.label for i in llll[0]], + merge = merge,) pbar.update(1) - # print(ttrace) + #print(ttrace) #print(llldict, lllldict) else: - for loop_index in loopindex(root1.level+1, root2.level+1): - # print(loop_index) - for i in range(lllloop[loop_index[0]]): - for j in range(llllloop[loop_index[1]]): - i_index = 0 - j_index = 0 - for i_tmp in range(loop_index[0]): - i_index += lllloop[i_tmp] - i_index += i - for j_tmp in range(loop_index[1]): - j_index += llllloop[j_tmp] - j_index += j - matrix_tmp = matrix_values[llldict[i_index], :] - matrix_tmp = matrix_tmp[:, lllldict[j_index]] + for loop_index in loopindex(root1.level+1,root2.level+1): + #print(loop_index) + for i in range(lllloop[loop_index[0]]): + for j in range(llllloop[loop_index[1]]): + i_index = 0 + j_index = 0 + for i_tmp in range(loop_index[0]): + i_index += lllloop[i_tmp] + i_index+=i + for j_tmp in range(loop_index[1]): + j_index += llllloop[j_tmp] + j_index+=j + matrix_tmp = matrix_values[llldict[i_index],:] + matrix_tmp = matrix_tmp[:,lllldict[j_index]] - matrix_values[i_index][j_index] = GetMaxScore(trace=trace_value, - root1=lll[loop_index[0]][i], - root2=llll[loop_index[1]][j], - allmatrix=matrix_values, - root1_index=i_index, - root2_index=j_index, - local_matrix=matrix_tmp, - local_matrix_root1_index=llldict[i_index], - local_matrix_root2_index=lllldict[j_index], - dict_score=score_dict, - Algorithm=Alg, - prune=pv, - lll_label=[ - i.label for i in lll[0]], - llll_label=[ - i.label for i in llll[0]], - merge=merge,) - # print(ttrace) + matrix_values[i_index][j_index] = GetMaxScore(trace=trace_value, + root1=lll[loop_index[0]][i], + root2=llll[loop_index[1]][j], + allmatrix = matrix_values, + root1_index=i_index, + root2_index=j_index, + local_matrix=matrix_tmp, + local_matrix_root1_index = llldict[i_index], + local_matrix_root2_index = lllldict[j_index], + dict_score=score_dict, + Algorithm=Alg, + prune=pv, + lll_label = [i.label for i in lll[0]], + llll_label = [i.label for i in llll[0]], + merge = merge,) + #print(ttrace) print(llldict) - - def changemat(rac, tracemat, tracemat_value, mat, list_tmp1, list_tmp2): + + def changemat(rac, tracemat, tracemat_value, mat, list_tmp1, list_tmp2, ttused): for i in tracemat_value[tuple(rac)]: - if isinstance(i[0], int) and isinstance(i[1], int): + if isinstance(i[0],int) and isinstance(i[1],int): list_tmp1.append(tracemat.index[i[0]]) + #print(tracemat.index[i[0]]) list_tmp2.append(tracemat.columns[i[1]]) - elif not isinstance(i[0], int) and not isinstance(i[1], int): + #print(tracemat.index[i[1]]) + elif not isinstance(i[0],int) and not isinstance(i[1],int): list_tmp1.append(tracemat.index[rac[0]]) + #print(tracemat.index[rac[0]]) list_tmp2.append(tracemat.columns[rac[1]]) return - - # print(i[0],i[0]) + + #print(i[0],i[0]) for i in tracemat_value[tuple(rac)]: - if isinstance(i[0], int) and isinstance(i[1], int): + ttused.append(str(i)) + if isinstance(i[0],int) and isinstance(i[1],int): #print(i[0],root1.leaf_count(), i[1],root2.leaf_count()) - if i == [] or (i[0] < root1.leaf_count() and i[1] < root2.leaf_count()): + if i == [] or (i[0] 0 and top < min(root1.node_count(), root2.node_count()): mat_tmp = deepcopy(matrix_values) mat_tmp2 = deepcopy(matrix_values) mat_tmp3 = deepcopy(matrix_values) - scorelist = [] + scorelist=[] for jjj in range(top): - # print(mat_tmp) + percent = 100 maxscore = np.max(mat_tmp) - del_i_index = np.where(mat_tmp == np.max(mat_tmp))[0][0] - del_j_index = np.where(mat_tmp == np.max(mat_tmp))[1][0] - + #print(mat_tmp) + del_i_index = np.where(mat_tmp==np.max(mat_tmp))[0][0] + del_j_index = np.where(mat_tmp==np.max(mat_tmp))[1][0] + list_tmp1 = [] list_tmp2 = [] - mat_tmp[del_i_index, del_j_index] = -99999. - changemat([del_i_index, del_j_index], ttrace, - trace_value, mat_tmp, list_tmp1, list_tmp2) - - tree_tmp1 = ['('] - tree_tmp2 = ['('] - mat_tmp2[del_i_index, del_j_index] = -99999. - getmatchtree([del_i_index, del_j_index], lllnode_obj, - llllnode_obj, trace_value, mat_tmp2, tree_tmp1, tree_tmp2) - - tree_tmp3 = ['('] - tree_tmp4 = ['('] - mat_tmp3[del_i_index, del_j_index] = -99999. - getmatchtree([del_i_index, del_j_index], lllnode_label, - llllnode_label, trace_value, mat_tmp3, tree_tmp3, tree_tmp4) - - # if list_tmp1[0] != lllnode[del_i_index].label: + mat_tmp[del_i_index,del_j_index] = -99999. + ttused = [str([del_i_index, del_j_index])] + changemat([del_i_index,del_j_index],ttrace, trace_value,mat_tmp, list_tmp1, list_tmp2, ttused) + + tree_tmp1 = [] + tree_tmp2 = [] + mat_tmp2[del_i_index,del_j_index] = -99999. + getmatchtree([del_i_index,del_j_index],lllnode_obj, llllnode_obj, trace_value,mat_tmp2, tree_tmp1, tree_tmp2) + + tree_tmp3 = [] + tree_tmp4 = [] + mat_tmp3[del_i_index,del_j_index] = -99999. + getmatchtree([del_i_index,del_j_index],lllnode_label, llllnode_label, trace_value,mat_tmp3, tree_tmp3, tree_tmp4) + + #if list_tmp1[0] != lllnode[del_i_index].label: # list_tmp1.insert(0,lllnode[del_i_index].label) - # if list_tmp2[0] != llllnode[del_j_index].label: + #if list_tmp2[0] != llllnode[del_j_index].label: # list_tmp2.insert(0,llllnode[del_j_index].label) - if jjj > 0: - if len(scorelist[-1]['Root1_match']) == 0: - percent = 0 - else: - percent = (len(list(set(list_tmp1) - set(scorelist[-1]['Root1_match'])) + list(set( - scorelist[-1]['Root1_match']) - set(list_tmp1))) / len(scorelist[-1]['Root1_match']))*100. - if round(percent) < diff: - # print(round(percent)) + + if jjj > 0 and diff > 0: + for i in range(len(scorelist)): + percent = min(percent, len(set(scorelist[i]['used']) - set(ttused)) / len(scorelist[i]['used'])*100.) + + while percent < float(diff): maxscore = np.max(mat_tmp) - del_i_index = np.where(mat_tmp == np.max(mat_tmp))[0][0] - del_j_index = np.where(mat_tmp == np.max(mat_tmp))[1][0] - + if (maxscore + 99999.) < 0.01: + break + del_i_index = np.where(mat_tmp==np.max(mat_tmp))[0][0] + del_j_index = np.where(mat_tmp==np.max(mat_tmp))[1][0] + list_tmp1 = [] list_tmp2 = [] - mat_tmp[del_i_index, del_j_index] = -99999. - changemat([del_i_index, del_j_index], ttrace, - trace_value, mat_tmp, list_tmp1, list_tmp2) - - tree_tmp1 = ['('] - tree_tmp2 = ['('] - mat_tmp2[del_i_index, del_j_index] = -99999. - getmatchtree([del_i_index, del_j_index], lllnode_obj, - llllnode_obj, trace_value, mat_tmp2, tree_tmp1, tree_tmp2) - - tree_tmp3 = ['('] - tree_tmp4 = ['('] - mat_tmp3[del_i_index, del_j_index] = -99999. - getmatchtree([del_i_index, del_j_index], lllnode_label, - llllnode_label, trace_value, mat_tmp3, tree_tmp3, tree_tmp4) - - tree_tmp1.append(');') - tree_tmp2.append(');') - tree_tmp3.append(');') - tree_tmp4.append(');') - - scorelist.append({'Score': maxscore, - 'Root1_label': lllnode[del_i_index].label + ';', - 'Root1_node': lllnode[del_i_index].nodeobj + ';', - 'Root1_seq': olllnode[del_i_index].nodeobj + ';', - 'Root1_label_node': label_leaves_list_to_tree(lllnode[del_i_index].leaves_label([]), lllnode[del_i_index].nodeobj) + ';', - 'Root2_label': llllnode[del_j_index].label + ';', - 'Root2_node': llllnode[del_j_index].nodeobj + ';', - 'Root2_seq': ollllnode[del_j_index].nodeobj + ';', - 'Root2_label_node': label_leaves_list_to_tree(llllnode[del_j_index].leaves_label([]), llllnode[del_j_index].nodeobj) + ';', - 'Root1_match': list_tmp1, - 'Root2_match': list_tmp2, - 'Root1_match_tree': ''.join(tree_tmp1), - 'Root2_match_tree': ''.join(tree_tmp2), - 'Root1_match_label_tree': ''.join(tree_tmp3), - 'Root2_match_label_tree': ''.join(tree_tmp4), - 'Root1_prune': where_prune(list_tmp1, list(map(lambda x: x.label, lllnode[del_i_index].leaves([])))), - 'Root2_prune': where_prune(list_tmp2, list(map(lambda x: x.label, llllnode[del_j_index].leaves([])))), - 'row': del_i_index, - 'col': del_j_index}) - - # print(ttrace) - # print(mat_tmp) - # print(diff) - # print(root1.node_count()*root2.node_count()) - return({'matrix': mmatrix, + mat_tmp[del_i_index,del_j_index] = -99999. + ttused = [str([del_i_index, del_j_index])] + changemat([del_i_index,del_j_index],ttrace, trace_value,mat_tmp, list_tmp1, list_tmp2, ttused) + + tree_tmp1 = [] + tree_tmp2 = [] + mat_tmp2[del_i_index,del_j_index] = -99999. + getmatchtree([del_i_index,del_j_index],lllnode_obj, llllnode_obj, trace_value,mat_tmp2, tree_tmp1, tree_tmp2) + + tree_tmp3 = [] + tree_tmp4 = [] + mat_tmp3[del_i_index,del_j_index] = -99999. + getmatchtree([del_i_index,del_j_index],lllnode_label, llllnode_label, trace_value,mat_tmp3, tree_tmp3, tree_tmp4) + percent = 100 + for i in range(len(scorelist)): + percent = min(percent, len(set(scorelist[i]['used']) - set(ttused)) / len(scorelist[i]['used'])*100.) + + print('不同率:') + print(percent) + tree_tmp1.append(';') + tree_tmp2.append(';') + tree_tmp3.append(';') + tree_tmp4.append(';') + #print(ttused) + + scorelist.append({'Score':maxscore, + 'Root1_label':lllnode[del_i_index].label + ';', + 'Root1_node':lllnode[del_i_index].nodeobj + ';', + 'Root1_seq':olllnode[del_i_index].nodeobj + ';', + 'Root1_label_node': label_leaves_list_to_tree(lllnode[del_i_index].leaves_label([]), lllnode[del_i_index].nodeobj) + ';', + 'Root2_label':llllnode[del_j_index].label + ';', + 'Root2_node':llllnode[del_j_index].nodeobj + ';', + 'Root2_seq':ollllnode[del_j_index].nodeobj + ';', + 'Root2_label_node': label_leaves_list_to_tree(llllnode[del_j_index].leaves_label([]), llllnode[del_j_index].nodeobj) + ';', + 'Root1_match': list_tmp1, + 'Root2_match': list_tmp2, + 'Root1_match_tree': ''.join(tree_tmp1), + 'Root2_match_tree': ''.join(tree_tmp2), + 'Root1_match_label_tree': ''.join(tree_tmp3), + 'Root2_match_label_tree': ''.join(tree_tmp4), + 'Root1_prune':where_prune(list_tmp1, list(map(lambda x:x.label,lllnode[del_i_index].leaves([])))), + 'Root2_prune':where_prune(list_tmp2, list(map(lambda x:x.label,llllnode[del_j_index].leaves([])))), + 'row':del_i_index, + 'col':del_j_index, + 'used':ttused + }) + + + + #print(ttrace) + #print(mat_tmp) + #print(diff) + #print(root1.node_count()*root2.node_count()) + return({'matrix':mmatrix, 'tree1_leaves_nodename': oroot1_label2celltype[1], 'tree1_leaves_label': root1_label2celltype[0], 'tree1_leaves_celltype': root1_label2celltype[1], 'tree2_leaves_nodename': oroot1_label2celltype[1], 'tree2_leaves_label': root2_label2celltype[0], 'tree2_leaves_celltype': root2_label2celltype[1], - 'score_dict': score_dict, - 'TopScoreList': scorelist}) + 'score_dict':score_dict, + 'TopScoreList':scorelist}) + + else: print("Parameter top cannot be negative, or it might be out of range.") + - + def FindNode(Seq: str, times: int) -> list: result = [] if Seq.find('(') == -1: @@ -820,7 +801,7 @@ def FindNode(Seq: str, times: int) -> list: brackets_num = 0 # 括号个数 node_num = -1 # 节点序号 node_tmp = '' - + for i in list(Seq): if i == '(': brackets_num += 1 @@ -867,17 +848,17 @@ def FindNode(Seq: str, times: int) -> list: class OP: - def __init__(self, - seq1_list_result, - seq2_list_result, - ScoreDictFile, - poolnum=1, - mav: float = 2., - miv: float = -1., - pv: float = -1., - notebook: int = 0, - Tqdm: int = 1, - merge: float = 10.,): + def __init__(self, + seq1_list_result, + seq2_list_result, + ScoreDictFile, + poolnum=1, + mav: float = 2., + miv: float = -1., + pv: float = -1., + notebook:int = 0, + Tqdm:int = 1, + merge:float=10.,): # 直接调用 Manager 提供的 list() 和 dict() self.manager = mp.Manager self.mp_lst = self.manager().list() @@ -920,11 +901,9 @@ def Foo(self, i, j, scoredictfile): score_dict = {} if scoredictfile == '': - score_dict = Scoredict(root1_tmp.leaves( - []), root2_tmp.leaves([]), self.mav, self.miv) + score_dict = Scoredict(root1_tmp.leaves([]), root2_tmp.leaves([]), self.mav, self.miv) else: - score_dict = QuantitativeScoreFile(root1_tmp.leaves( - []), root2_tmp.leaves([]), self.mav, self.miv, ScoreDictFile) + score_dict = QuantitativeScoreFile(root1_tmp.leaves([]), root2_tmp.leaves([]), self.mav, self.miv, ScoreDictFile) mmatrix = pd.DataFrame([[0.0 for i in range(len(llllnode_tmp))] for j in range(len(lllnode_tmp))], index=[i.nodeobj for i in lllnode_tmp], @@ -967,11 +946,9 @@ def Foo(self, i, j, scoredictfile): dict_score=score_dict, prune=self.pv, Algorithm='', - lll_label=[ - i.label for i in lll[0]], - llll_label=[ - i.label for i in llll[0]], - merge=self.merge) + lll_label = [i.label for i in lll[0]], + llll_label = [i.label for i in llll[0]], + merge = self.merge) # print(mmatrix) # print(ttrace) # print(matrix_values[len(lllnode)-1][len(llllnode)-1]) @@ -989,7 +966,7 @@ def flow(self): update = lambda *args: pbar.update() else: update = None - + for i, j in zip(self.Seq1_list, self.Seq2_list): pool.apply_async(func=self.Foo, args=( i, j, self.scoredictfile), callback=update) @@ -998,16 +975,16 @@ def flow(self): pool.join() -def pvalue(times: int, - topscorelist, - ScoreDictFile: str = '', - CPUs: int = 50, - mav: float = 2., - miv: float = -1., +def pvalue(times: int, + topscorelist, + ScoreDictFile: str = '', + CPUs: int = 50, + mav: float = 2., + miv: float = -1., pv: float = -1., notebook: int = 0, Tqdm: int = 1, - merge: float = 10., + merge:float = 10., ): Seq1_list_result_max = [] Seq2_list_result_max = [] @@ -1016,7 +993,7 @@ def pvalue(times: int, Seq2_list_result_max.append(FindNode(i['Root2_node'], times)) score_list_max = [] - # print(len(topscorelist)) + #print(len(topscorelist)) for i in range(len(topscorelist)): op_max = OP( Seq1_list_result_max[i], Seq2_list_result_max[i], ScoreDictFile, CPUs, mav, miv, pv, notebook, Tqdm, merge) @@ -1028,69 +1005,62 @@ def pvalue(times: int, if __name__ == '__main__': - example = scoremat(TreeSeqFile=TreeSeqFile, - TreeSeqFile2=TreeSeqFile2, - Name2TypeFile=Name2TypeFile, - Name2TypeFile2=Name2TypeFile2, - ScoreDictFile=ScoreDictFile, - mav=mav, - miv=miv, - top=top, - notebook=notebook, - pv=pv, - Tqdm=Tqdm, - diff=diff, - merge=merge) + example = scoremat(TreeSeqFile = TreeSeqFile, + TreeSeqFile2 = TreeSeqFile2, + Name2TypeFile = Name2TypeFile, + Name2TypeFile2 = Name2TypeFile2, + ScoreDictFile = ScoreDictFile, + mav = mav, + miv = miv, + top = top, + notebook = notebook, + pv = pv, + Tqdm = Tqdm, + diff = diff, + merge = merge) print("\n********** Score Matrix **********\n", example['matrix']) - + if top == 0: print("\n********** T1root_T2root **********") - for key, value in example['T1root_T2root'][0].items(): - print('{key}:{value}'.format(key=key, value=value)) - print("\n********** tree1_leaves_nodename **********\n", - example['tree1_leaves_nodename']) - print("\n********** tree1_leaves_label **********\n", - example['tree1_leaves_label']) - print("\n********** tree1_leaves_celltype **********\n", - example['tree1_leaves_celltype']) - print("\n********** tree2_leaves_nodename **********\n", - example['tree2_leaves_nodename']) - print("\n********** tree2_leaves_label **********\n", - example['tree2_leaves_label']) - print("\n********** tree2_leaves_celltype **********\n", - example['tree2_leaves_celltype']) - print("\n********** score_dict **********\n", example['score_dict']) + for key,value in example['T1root_T2root'][0].items(): + print('{key}:{value}'.format(key = key, value = value)) + print("\n********** tree1_leaves_nodename **********\n", example['tree1_leaves_nodename']) + print("\n********** tree1_leaves_label **********\n", example['tree1_leaves_label']) + print("\n********** tree1_leaves_celltype **********\n", example['tree1_leaves_celltype']) + print("\n********** tree2_leaves_nodename **********\n", example['tree2_leaves_nodename']) + print("\n********** tree2_leaves_label **********\n", example['tree2_leaves_label']) + print("\n********** tree2_leaves_celltype **********\n", example['tree2_leaves_celltype']) + print("\n********** score_dict **********\n", example['score_dict']) elif top > 0: - for index, keyss in enumerate(example['TopScoreList']): + for index,keyss in enumerate(example['TopScoreList']): print('\n********** Top ', index+1, '**********') - for key, value in keyss.items(): - print('{key}:{value}'.format(key=key, value=value)) - - print("\n********** tree1_leaves_nodename **********\n", - example['tree1_leaves_nodename']) - print("\n********** tree1_leaves_label **********\n", - example['tree1_leaves_label']) - print("\n********** tree1_leaves_celltype **********\n", - example['tree1_leaves_celltype']) - print("\n********** tree2_leaves_nodename **********\n", - example['tree2_leaves_nodename']) - print("\n********** tree2_leaves_label **********\n", - example['tree2_leaves_label']) - print("\n********** tree2_leaves_celltype **********\n", - example['tree2_leaves_celltype']) - print("\n********** score_dict **********\n", example['score_dict']) + for key,value in keyss.items(): + print('{key}:{value}'.format(key = key, value = value)) + + print("\n********** tree1_leaves_nodename **********\n", example['tree1_leaves_nodename']) + print("\n********** tree1_leaves_label **********\n", example['tree1_leaves_label']) + print("\n********** tree1_leaves_celltype **********\n", example['tree1_leaves_celltype']) + print("\n********** tree2_leaves_nodename **********\n", example['tree2_leaves_nodename']) + print("\n********** tree2_leaves_label **********\n", example['tree2_leaves_label']) + print("\n********** tree2_leaves_celltype **********\n", example['tree2_leaves_celltype']) + print("\n********** score_dict **********\n", example['score_dict']) + if times > 0: - ppp = pvalue(times=times, - topscorelist=example['T1root_T2root'] if top == 0 else example['TopScoreList'], - ScoreDictFile=ScoreDictFile, - CPUs=CPUs, - mav=mav, - miv=miv, - pv=pv, - notebook=notebook, - Tqdm=Tqdm) + ppp = pvalue(times = times, + topscorelist = example['T1root_T2root'] if top == 0 else example['TopScoreList'], + ScoreDictFile = ScoreDictFile, + CPUs = CPUs, + mav = mav, + miv = miv, + pv = pv, + notebook = notebook, + Tqdm = Tqdm) print("\n********** P Value **********") print(ppp) - # print(psutil.cpu_count(False)) + #print(psutil.cpu_count(False)) + + time_end = time.time() # 记录结束时间 + time_sum = time_end - time_start # 计算的时间差为程序的执行时间，单位为秒/s + print('程序总共运行时间为:'+ str(time_sum) + 's') \ No newline at end of file diff --git a/mdelta/mymath.py b/mdelta/mymath.py old mode 100644 new mode 100755 diff --git a/mdelta/mytest.py b/mdelta/mytest.py old mode 100644 new mode 100755