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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)...

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