How to use code2string method in fMBT

Best Python code snippet using fMBT_python

util.py

Source:util.py

...88                        parent_name=name89                node_3thlayer=dict(name=parent_name,children=[])90                node_2thlayer['children'].append(node_3thlayer)91                for children in value:92                    node_4thlayer=dict(name=code2string(children,curs),children=[])93                    node_3thlayer['children'].append(node_4thlayer)94        else:95            # three layer96           97            node_2thlayer=dict(name=parent_name,children=[])98            snomed_hierarchy['children'].append(node_2thlayer)99            for concept in cluster_map[cluster_map['cluster']==i]['concept_name']:100                node_3thlayer=dict(name=concept,children=[])101                node_2thlayer['children'].append(node_3thlayer)    102    json.dump(snomed_hierarchy, open(os.path.join("static","data","snomed_hierarchy_four_layer_tree.json"), "w"))103# three-layer-tree104def build_snomed_hierarchy_three_layer_tree(snomed_codes,curs,p2c):105    106    node_dic={}107    for code in snomed_codes:108        concept=code2string(code,curs)109        top_concept=code2topconcept(code,curs)110        if concept !="":111            if top_concept not in node_dic:112                node_dic[top_concept]=[concept]113            elif concept not in node_dic[top_concept]:114                node_dic[top_concept].append(concept)115    snomed_hierarchy={'name':'snomed','children':[]}116    for key,value in node_dic.items():117        node_2thlayer=dict(name=key,children=[])118        snomed_hierarchy['children'].append(node_2thlayer)119        for each in value:120            node_3rdlayer=dict(name=each,children=[])121            node_2thlayer['children'].append(node_3rdlayer)122    json.dump(snomed_hierarchy, open(".\\static\\data\\snomed_hierarchy_three_layer_tree.json", "w"))123# first version four layer tree- seperate 124def build_snomed_hierarchy_four_layer_tree_no_name(snomed_codes,top_level,curs,p2c):125    #check the number of independent cluster:126    # find cluster127    cluster_dic={}128    model=snomed_embedding()129    node_dic={}130    for top_code in top_level:131        node_dic[top_code]=[]132        childrens=p2c[top_code]133        for children in childrens:134            get_all_the_children_concept(top_code,children,p2c,snomed_codes,node_dic)135    snomed_hierarchy={'name':'snomed','children':[]}136    for key, value in node_dic.items():137        top_concept=code2string(key,curs)138        X = np.zeros(shape=(1,200))139        snomed_vector={}140        label_list=[]141        for code in value:142            concept =code2string(code,curs)143            if concept !="" and code in model.keys():144                label_list.append(concept)145                X=np.concatenate((X,model[code].reshape(1,200)),axis=0) 146        147        X=np.delete(X,0,axis=0)148        if(len(X)!=0):149            cluster_num=int(len(value)/3)+1150            kmeans = KMeans(n_clusters=cluster_num, init='k-means++', max_iter=300, n_init=10, random_state=0)151            pred_y = kmeans.fit_predict(X)152            cluster_map = pd.DataFrame()153            cluster_map['concept_name'] = label_list154            cluster_map['cluster'] = kmeans.labels_155       156            node_2thlayer=dict(name=top_concept,children=[])157            snomed_hierarchy['children'].append(node_2thlayer)158            for i in range(cluster_num):159                node_3thlayer=dict(name="",children=[])160                node_2thlayer['children'].append(node_3thlayer)161                for each in cluster_map[cluster_map.cluster == i]['concept_name']:162                    node_4rdlayer=dict(name=each,children=[])163                    node_3thlayer['children'].append(node_4rdlayer)164            cluster_dic[top_concept]=cluster_map165    json.dump(snomed_hierarchy, open(".\\static\\data\\snomed_hierarchy_four_layer_tree.json", "w"))166    return cluster_dic167    # three-layer-tree168def build_snomed_hierarchy_three_layer_tree(snomed_codes,curs,p2c):169    170    node_dic={}171    for code in snomed_codes:172        concept=code2string(code,curs)173        top_concept=code2topconcept(code,curs)174        if concept !="":175            if top_concept not in node_dic:176                node_dic[top_concept]=[concept]177            elif concept not in node_dic[top_concept]:178                node_dic[top_concept].append(concept)179    snomed_hierarchy={'name':'snomed','children':[]}180    for key,value in node_dic.items():181        node_2thlayer=dict(name=key,children=[])182        snomed_hierarchy['children'].append(node_2thlayer)183        for each in value:184            node_3rdlayer=dict(name=each,children=[])185            node_2thlayer['children'].append(node_3rdlayer)186    json.dump(snomed_hierarchy, open(".\\static\\data\\snomed_hierarchy_three_layer_tree.json", "w"))187    # the original dag188def build_snomed_hierarchy_dag(snomed_codes,top_level,curs,p2c):189    190    edge={}191    tagged_nodes={}192    with open(".\\static\\data\\DAG\\dag.dot", "w") as file:193        file.write('digraph g {'+'\n')194        for top_code in top_level:195            add_snomed_node_for_dag(top_code,'snomed',file,curs,p2c,edge,snomed_codes,tagged_nodes)196        file.write('}')197    198    #json.dump(edge, open(".\\static\\data\\edge.json", "w"))199def build_snomed_hierarchy_tree(snomed_codes,top_level,curs,p2c):200    201    snomed_hierarchy={'name':'snomed','children':[]}202    for top_code in top_level:203        add_snomed_node(top_code,snomed_hierarchy,curs,p2c)204        205    json.dump(snomed_hierarchy, open(".\\static\\data\\snomed_hierarchy.json", "w"))206    def add_snomed_node_for_dag_three_layers(top_concept,node,file,curs,p2c,snomed_codes,node_dic):207    if node not in node_dic.keys():208        concept=code2string(node,curs)209        if concept !='':210            if node in snomed_codes:211                concept=code2string(node,curs)212                file.write(top_concept+'->'+concept+';\n')213                node_dic[node]=1214            if node in p2c.keys():215                childrens=p2c[node]216                for children in childrens:217                    add_snomed_node_for_dag_three_layers(top_concept,children,file,curs,p2c,snomed_codes,node_dic)218'''219three layer built using dot220def build_snomed_hierarchy_three_layer_dag(snomed_codes,top_level,curs,p2c):221    node_dic={}222    with open(".\\static\\data\\dag_three_layers.dot", "w") as file:223        file.write('digraph g {'+'\n')224        for top_code in top_level:225            concept=code2string(top_code,curs)226            file.write('snomed->'+concept+';\n')227            childrens=p2c[top_code]228            for children in childrens:229                add_snomed_node_for_dag_three_layers(concept,children,file,curs,p2c,snomed_codes,node_dic)230        file.write('}')231def add_snomed_node_for_dag_three_layers(top_concept,node,file,curs,p2c,snomed_codes,node_dic):232    if node not in node_dic.keys():233        concept=code2string(node,curs)234        if concept !='':235            if node in snomed_codes:236                concept=code2string(node,curs)237                file.write(top_concept+'->'+concept+';\n')238                node_dic[node]=1239            if node in p2c.keys():240                childrens=p2c[node]241                for children in childrens:242                    add_snomed_node_for_dag_three_layers(top_concept,children,file,curs,p2c,snomed_codes,node_dic)243#dag 0244def build_clinical_finding_cluster_dag(snomed_codes,cluster_dic,curs,p2c):245    edge={}246    tagged_nodes={}247    color=['red','blue','orange','green','yellow','gray']248    with open(".\\static\\data\\DAG\\dag_clinical_finding.dot", "w") as file:249        file.write('digraph g {'+'\n')250        add_snomed_node_for_dag_non_color('404684003','snomed',file,curs,p2c,edge,snomed_codes,tagged_nodes)251        #color cluster252        top_concept=code2string('404684003',curs)253        cluster_map=cluster_dic[top_concept]254        255        for each in cluster_map.itertuples():256            concept=each[1]257            cluster=each[2]258            file.write(concept+' [style=filled, fillcolor='+color[cluster]+'];\n')259        file.write('}')260# dag 1261def build_substance_pharmecutical_dag(snomed_codes,cluster_dic,curs,p2c):262    edge={}263    tagged_nodes={}264    color=['aliceblue','antiquewhite','cyan', 'aquamarine','springgreen' ,'turquoise','violet','yellow','yellowgreen','tomato','darkorange','darkorchid','maroon','darksalmon','darkseagreen','darkslateblue','darkslategray','green','greenyellow','skyblue','goldenrod','lightslategray','mediumblue','royalblue','red','mediumvioletred']265    with open(".\\static\\data\\DAG\\substance_pharmaceutical.dot", "w") as file:266        file.write('digraph g {'+'\n')267        for top_code in ['105590001','373873005']:268            add_snomed_node_for_dag_non_color(top_code,'snomed',file,curs,p2c,edge,snomed_codes,tagged_nodes)269       270        271        node_dic={}272        for top_code in ['105590001','373873005']:273            node_dic[top_code]=[]274            childrens=p2c[top_code]275            for children in childrens:276                get_all_the_children_concept(top_code,children,p2c,snomed_codes,node_dic)277        model=snomed_embedding()278        X = np.zeros(shape=(1,200))279        label_list=[]280        for key, value in node_dic.items():281            for code in value:282                concept =code2string(code,curs)283                if concept !="" and code in model.keys():284                    label_list.append(concept)285                    X=np.concatenate((X,model[code].reshape(1,200)),axis=0) 286        X=np.delete(X,0,axis=0)287        if(len(X)!=0):288            cluster_num=int(len(label_list)/3)+1289            kmeans = KMeans(n_clusters=cluster_num, init='k-means++', max_iter=300, n_init=10, random_state=0)290            pred_y = kmeans.fit_predict(X)291            cluster_map = pd.DataFrame()292            cluster_map['concept_name'] = label_list293            cluster_map['cluster'] = kmeans.labels_294            #color cluster295            296        for each in cluster_map.itertuples():297            concept=each[1]298            cluster=each[2]299            file.write(concept+' [style=filled, fillcolor='+color[cluster]+'];\n')300        file.write('}')301# dag 2302'''303def word2aui(word):304    #word2sui={}305    #for word in words:306        #better use API?307    f = open("MRCONSO.RRF",'rb')308    aui_index={}309    for line in f.readlines():310        line=str(line)311        content = re.compile("^b'(.*)'$", re.S)312        content=re.findall(content, line)313        if (content==[]):314            content = re.compile('^b\"(.*)\"$', re.S)315            content=re.findall(content, line)316        content=content[0].strip('\\n')317        content=content.split('|')318        string=content[14].lower()319        if re.search(word,string):320            aui_index[content[7]]={}321            aui_index[content[7]]['CUI']=content[0]322            aui_index[content[7]]['LAT']=content[1]323            aui_index[content[7]]['TS']=content[2]324            aui_index[content[7]]['LUI']=content[3]325            aui_index[content[7]]['STT']=content[4]326            aui_index[content[7]]['SUI']=content[5]327            aui_index[content[7]]['ISPREF']=content[6]328            aui_index[content[7]]['SAUI']=content[8]329            aui_index[content[7]]['SCUI']=content[9]330            aui_index[content[7]]['SDUI']=content[10]331            aui_index[content[7]]['SAB']=content[11]332            aui_index[content[7]]['TTY']=content[12]333            aui_index[content[7]]['CODE']=content[13]334            aui_index[content[7]]['STR']=content[14]335            aui_index[content[7]]['SRL']=content[15]336            aui_index[content[7]]['SUPPRESS']=content[16]337            aui_index[content[7]]['CVF']=content[17]338    return aui_index339    340def sui2cui(aui_index):341    sui2cui={}342    for item in aui_index.values():343        if item['SUI']+' '+item['STR'] not in sui2cui.keys():344            345            sui2cui[item['SUI']+' '+item['STR']]=[item['CUI']]346        else:347            sui2cui[item['SUI']+' '+item['STR']].append(item['CUI'])348    return sui2cui349 def add_node(node, parent ):350    # First create the new node and append it to its parent's children351    newNode = dict( node_id=node.id, children=[] )352    parent["children"].append( newNode )353    # Recursively add the current node's children354    if node.left: add_node( node.left, newNode )355    if node.right: add_node( node.right, newNode )356def label_tree( n ,id2name):357    # If the node is a leaf, then we have its name358    if len(n["children"]) == 0:359        n["name"] = id2name[n["node_id"]] 360    if len(n["children"])==1:361        label_tree(n["children"][0],id2name)362        n["name"] = ""363    if len(n["children"])==2:364        label_tree(n["children"][0],id2name)365        label_tree(n["children"][1],id2name)366        n["name"] = ""367    368    # If not, flatten all the leaves in the node's subtree369    #else:370    #    leafNames = reduce(lambda ls, c: ls + label_tree(c,id2name), n["children"], [])371    # Delete the node id since we don't need it anymore and372    # it makes for cleaner JSON373    del n["node_id"]374    # Labeling convention: "-"-separated leaf names375    #n["name"] = name = "-".join(sorted(map(str, leafNames)))376    377 def add_snomed_node_for_dag(node,parent,file,curs,p2c,edge_dic,snomed_codes,tagged_nodes):378    379    concept=code2string(node,curs)380    if concept !="":381        edge=parent+concept382        if edge not in edge_dic.keys():383            file.write(parent+'->'+concept+';\n')384            if node in snomed_codes and node not in tagged_nodes.keys():385                file.write(concept+' [style=filled, fillcolor=green];\n')386                tagged_nodes[node]=1387            edge_dic[edge]=1388            if node in p2c.keys():389                childrens=p2c[node]390                for children in childrens:391                    add_snomed_node_for_dag(children,concept,file,curs,p2c,edge_dic,snomed_codes,tagged_nodes)392                    def add_snomed_node(node,parent,curs,p2c):393    concept=code2string(node,curs)394    if concept !="" :395        newNode=dict(name=concept,children=[])396        parent["children"].append(newNode)397        if node in p2c.keys():398            childrens=p2c[node]399            for children in childrens:400                add_snomed_node(children,newNode,curs,p2c)401                def snomed_collocation(parsed_result,curs):402    '''403    word_frequency={}404    collocation_frequency={}405   406    for i in range(len(parsed_result['docs'])):407        for j in range(len(parsed_result['docs'][i]['code'])):408            cui_1=parsed_result['docs'][i]['code'][j]409            # calculate word frequency410            if (cui_1 not in word_frequency.keys()):411                word_frequency[cui_1]=1412            else:413                word_frequency[cui_1]+=1414            # calculate collocation frequency415            for cui_2 in parsed_result['docs'][i]['code'][j+1:]:416                if (cui_1,cui_2) in collocation_frequency.keys():417                    collocation_frequency[(cui_1,cui_2)]+=1418                elif (cui_2,cui_1) in collocation_frequency.keys():419                    collocation_frequency[(cui_2,cui_1)]+=1420                else:421                    collocation_frequency[(cui_1,cui_2)]=1422    #calculate PMI:423    PMI={}424    for key,value in collocation_frequency.items():425        print(key)426        '''427    model=snomed_embedding()428    label_list=[]429    lacked_label_list=[]430    matrix = np.zeros(shape=(1,200))431    snomed_vector={}432    snomed_codes=[]433   434    for i in range(len(parsed_result['docs'])):435        print("process {} documents".format(i))436      437        for snomed_code in parsed_result['docs'][i]['code']:438            concept=code2string(snomed_code,curs)439            if concept !="" :440                if snomed_code not in snomed_codes:441                    snomed_codes.append(snomed_code)442                if concept not in snomed_vector.keys():443                    if snomed_code in model.keys():444                        snomed_vector[concept]=model[snomed_code].reshape(1,200) 445                    elif snomed_code not in lacked_label_list:446                        lacked_label_list.append(snomed_code)447    for key,value in snomed_vector.items():448        label_list.append(key)449        matrix=np.concatenate((matrix,value),axis=0)450    matrix=np.delete(matrix,0,axis=0)451    clusters = linkage(matrix, 'single')452    #dendrogram(linked,orientation='top',labels=label_list,distance_sort='ascending', orientation = 'right')453   454    T = to_tree( clusters , rd=False )455    d3Dendro = dict(children=[], name="cui-code")456    add_node( T, d3Dendro ) 457    id2name = dict(zip(range(len(label_list)), label_list)) 458    label_tree( d3Dendro["children"][0], id2name)459    # Output to JSON460    json.dump(d3Dendro, open(".\\static\\data\\snomed_cluster.json", "w"), sort_keys=True, indent=4)461    return snomed_codes462    def cui_collocation(parsed_result):463    model=cui_embedding()464    label_list=[]465    lacked_label_list=[]466    matrix = np.zeros(shape=(1,500))467    API_KEY="35320203-e665-46d7-b21f-0c1fa1260f22"468    result=requests.post("https://utslogin.nlm.nih.gov/cas/v1/api-key",{'apikey':API_KEY}).text469    soup=Soup(result)470    TGT_KEY=soup.form['action']471    cui_vector={}472    for i in range(len(parsed_result['docs'])):473        print("process {} documents".format(i))474      475        for cui in parsed_result['docs'][i]['cui']:476            concept=cui2word(cui,TGT_KEY)477            if concept not in cui_vector.keys():478                if cui in model.index:479                    cui_vector[concept]=model.loc[cui].as_matrix().reshape(1,500) 480                elif cui not in lacked_label_list:481                    lacked_label_list.append(cui)482    for key,value in cui_vector.items():483        label_list.append(key)484        matrix=np.concatenate((matrix,value),axis=0)485    matrix=np.delete(matrix,0,axis=0)486    clusters = linkage(matrix, 'single')487    #dendrogram(linked,orientation='top',labels=label_list,distance_sort='ascending', orientation = 'right')488    T = to_tree( clusters , rd=False )489    d3Dendro = dict(children=[], name="cui-code")490    add_node( T, d3Dendro ) 491    id2name = dict(zip(range(len(label_list)), label_list)) 492    label_tree( d3Dendro["children"][0], id2name)493    # Output to JSON494    json.dump(d3Dendro, open(".\\static\\data\\d3-dendrogram.json", "w"), sort_keys=True, indent=4)495def snomed_collocation(parsed_result,curs):496    497    model=snomed_embedding()498    label_list=[]499    lacked_label_list=[]500    matrix = np.zeros(shape=(1,200))501    snomed_vector={}502    snomed_codes=[]503   504    for i in range(len(parsed_result['docs'])):505        print("process {} documents".format(i))506      507        for snomed_code in parsed_result['docs'][i]['code']:508            concept=code2string(snomed_code,curs)509            if concept !="" :510                if snomed_code not in snomed_codes:511                    snomed_codes.append(snomed_code)512                if concept not in snomed_vector.keys():513                    if snomed_code in model.keys():514                        snomed_vector[concept]=model[snomed_code].reshape(1,200) 515                    elif snomed_code not in lacked_label_list:516                        lacked_label_list.append(snomed_code)517    for key,value in snomed_vector.items():518        label_list.append(key)519        matrix=np.concatenate((matrix,value),axis=0)520    matrix=np.delete(matrix,0,axis=0)521    clusters = linkage(matrix, 'single')522    #dendrogram(linked,orientation='top',labels=label_list,distance_sort='ascending', orientation = 'right')...

pcaefin.py

Source:pcaefin.py

...25                i += 126            string_list.append(string[:-4])27    return string_list28#å°ç¼ç è½¬æ¢ä¸ºåç¬¦29def code2string(code,code_len):30    string = ''31    for j in xrange(0,code_len,2):32        string += chr(int((code[j]+code[j+1]),16))33    return string34fl = walk('script')35for fn in fl:36    #è¿æ»¤étxtæä»¶37    if not fnmatch.fnmatch(fn,'*.txt'):38        continue39    print fn40    lines = codecs.open(fn,'rb','utf16').readlines()41    #è·å¾ææ¬åè¡¨42    string_list = makestr(lines)43    44    dst = open(fn[:-4],'rb+')45    start = 0x3395aa46    indexoffset = 0x7a8f0647    dst.seek(start)48    offset_list = []49    j = 050    #éæ¡åå¥ææ¬åè¡¨éé¢çææ¬51    for string in string_list:52        offset = dst.tell()-start53        charlist = []54        len_string = len(string)55        i = 056        while i < len_string:57            char = string[i]58 59            if char == u'â²':60                charlist.append(code2string('07000800',8))61            elif char == u'â³':62                charlist.append(code2string('07000900',8))63            elif char == u'â':64                charlist.append(code2string('07000400',8))65            elif char == u'â½':66                charlist.append(code2string('0000',4))67            elif char == u'â':68                charlist.append(code2string('07000600',8))69            elif char == u'â¼':70                charlist.append(code2string('0a00',4))71            elif char == u'â':72                charlist.append(code2string('07000100',8))73            elif char == '\r':74                charlist.append(code2string('0d00',4))75            elif char == '\n':76                charlist.append(code2string('0a00',4))77            else:78                charlist.append(char.encode('utf-16-le'))79            i += 180        charlist.append(code2string('0000',4))81        code = ''.join(charlist)82        dst.write(code)83        offset_list.append(struct.pack('I',offset))84        85    dst.write(struct.pack('I', len(offset_list)))86    for of in offset_list:87        dst.write(of)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.