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How to use select_parameters method in localstack

Best Python code snippet using localstack_python

select_parameters.py

Source:select_parameters.py

...40                            27347.0,41                            23436.0]}42optimized_codon_fop = {'correlation_analysis': []}43optimized_codon_cbi = {'correlation_analysis': []}44class select_parameters():45    cai_reference_frequency = reference_frequnecy_cai['Homo_sapiens_EMBOSS']46    tai_tGCN = tGCN_tai['Homo_sapiens_GRCh38_GtRNAdb']47    ite_heg = reference_heg_ite['Homo_sapiens_EMBOSS']48    ite_bg = reference_bg_ite['Homo_sapiens_EMBOSS']49    fop_opt_codon = optimized_codon_fop['correlation_analysis']50    cbi_opt_codon = optimized_codon_cbi['correlation_analysis']51    @staticmethod52    def parameter_select_cai():53        # select reference set for cai computing54        cai_dict = {1: 'Homo_sapiens_EMBOSS'}55        while True:56            try:57                # select reference set58                cai_choice = int(input("select reference set for cai computing"59                                       "\nYour choice is: "60                                       "\n(1) 'Homo_sapiens_reference_set_fromEMBOSS[DEFAULT]'\n"))61            except ValueError:62                print("Only one reference set could be chosen at a time.")63                continue64            if cai_choice not in cai_dict.keys():65                raise ValueError('the number you choose is out of range')66            select_parameters.cai_reference_frequency = reference_frequnecy_cai[cai_dict[cai_choice]]67            break68        print(f"reference set for cai computing was selected\n")69        pass70        # result +=(cai_reference_frequency,)71        # result +=(cai_reference_order,)72    @staticmethod73    def parameter_select_fop():74        # select optimized codons for fop computing75        fop_dict = {0: 'correlation_analysis'}76        while True:77            try:78                fop_choice = int(input("select optimized codons for fop computing"79                                       "\nYour choice is: "80                                       "\n(0) Correlation_analysis_for_optimized_codon[DEFAULT]"81                                       "\n"))82            except ValueError:83                print("Only one reference could be chosen at a time.")84                continue85            select_parameters.fop_opt_codon = optimized_codon_cbi[fop_dict[fop_choice]]86            break87        print(f"optimized codon for fop computing was selected\n")88    @staticmethod89    def parameter_select_tai():90        # select reference GCN of tRNAs for tai computing91        tai_dict = {1: 'Homo_sapiens_GRCh38_GtRNAdb'}92        while True:93            try:94                tai_choice = int(input("select reference GCN of tRNAs for tai computing"95                                       "\nYour choice is: "96                                       "\n(1) Homo_sapiens_GRCh38_fromGtRNAdb[DEFAULT]"97                                       "\n"))98            except ValueError:99                print("Only one reference GCN could be chosen at a time.")100                continue101            select_parameters.tai_tGCN = tai_dict[tai_choice]102            break103        print("the GCN of tRNA for tai computing was selected\n")104    @staticmethod105    def parameter_select_ite():106        # select reference set for ite computing107        ite_dict = {1: 'Homo_sapiens_EMBOSS'}108        while True:109            try:110                ite_choice = int(input("select reference set for ite computing"111                                       "\nYour choice is: "112                                       "\n(1) 'Homo_sapiens_reference_fromEMBOSS[DEFAULT]'\n"))113            except ValueError:114                print("Only one reference set could be chosen at a time.")115                continue116            select_parameters.ite_heg = reference_heg_ite[ite_dict[ite_choice]]117            select_parameters.ite_bg = reference_bg_ite[ite_dict[ite_choice]]118            break119        print(f"reference set for ite computing was selected\n")120    @staticmethod121    def parameter_select_cbi():122        # select optimized codons for fop computing123        cbi_dict = {0: 'correlation_analysis'}124        while True:125            try:126                cbi_choice = int(input("select optimized codons for cbi computing"127                                       "\nYour choice is: "128                                       "\n(0) Correlation_analysis_for_optimized_codon[DEFAULT]"129                                       "\n"))130            except ValueError:131                print("Only one reference could be chosen at a time.")132                continue133            select_parameters.cbi_opt_codon = optimized_codon_cbi[cbi_dict[cbi_choice]]134            break135        print(f"optimized codon for cbi computing was selected\n")136    @staticmethod137    def process_parameter_file(parameter_file):138        # the input file welcomes an excel file or a dataframe,139        # namely if you have a parameter file in form of csv, make it a dataframe first.140        try:141            parameter_dataframe = pd.read_excel(parameter_file, index_col=0)142        except:143            if type(parameter_file) == pd.DataFrame:144                parameter_dataframe = parameter_file145            else:146                raise AttributeError('the input parameter file should be an excel file(.xlsx) or a dataframe.')147        # sorting the dataframe.148        parameter_dataframe = parameter_dataframe.loc[["TTT", "TTC", "TTA", "TTG",149                                                       "TCT", "TCC", "TCA", "TCG",150                                                       "TAT", "TAC", "TAA", "TAG",151                                                       "TGT", "TGC", "TGA", "TGG",152                                                       "CTT", "CTC", "CTA", "CTG",153                                                       "CCT", "CCC", "CCA", "CCG",154                                                       "CAT", "CAC", "CAA", "CAG",155                                                       "CGT", "CGC", "CGA", "CGG",156                                                       "ATT", "ATC", "ATA", "ATG",157                                                       "ACT", "ACC", "ACA", "ACG",158                                                       "AAT", "AAC", "AAA", "AAG",159                                                       "AGT", "AGC", "AGA", "AGG",160                                                       "GTT", "GTC", "GTA", "GTG",161                                                       "GCT", "GCC", "GCA", "GCG",162                                                       "GAT", "GAC", "GAA", "GAG",163                                                       "GGT", "GGC", "GGA", "GGG"]]164        # change parameters165        if 'cai_ref' in parameter_dataframe.columns:166            select_parameters.cai_reference_frequency = parameter_dataframe['cai_ref'].values.tolist()167        if 'tai_tGCN' in parameter_dataframe.columns:168            select_parameters.tai_tGCN = parameter_dataframe['tai_tGCN'].values.tolist()169        if 'ite_heg' in parameter_dataframe.columns:170            select_parameters.ite_heg = parameter_dataframe['ite_heg'].values.tolist()171        if 'ite_bg' in parameter_dataframe.columns:172            select_parameters.ite_bg = parameter_dataframe['ite_bg'].values.tolist()173        if 'cbi_opt' in parameter_dataframe.columns:174            # todo æ³¨æå¯¼å¥çæä»¶ä¸å®éè®¡ç®æ¶çå¯è½ä¸åãä½¿å¾cbiåfopå¨è®¡ç®æ¶è½å®¹çº³ä¸åçoptã175            select_parameters.cbi_opt_codon = parameter_dataframe['cbi_opt'].values.tolist()176        if 'fop_opt' in parameter_dataframe.columns:177            select_parameters.fop_opt_codon = parameter_dataframe['fop_opt'].values.tolist()178        return179    fundict = {1: parameter_select_cai, 2: parameter_select_tai, 3: parameter_select_ite, 4: parameter_select_fop,180               5: parameter_select_cbi}181    def __init__(self, parameter_file=None, cai_ref=None, tai_tGCN=None, ite_heg=None, ite_bg=None, fop_opt=None,182                 cbi_opt=None, skip_select=False):183        additional_request = [parameter_file, cai_ref, tai_tGCN, ite_heg, ite_bg, fop_opt, cbi_opt]184        # if skip_select is True, you will select parameters among the prestored database.185        if not skip_select:186            while True:187                try:188                    indexes = list(189                        map(int, input("Press the serial number of index of which parameter you want to change: "190                                       "\n(1) cai "191                                       "\n(2) tai "192                                       "\n(3) ite "193                                       "\n(4) fop "194                                       "\n(5) cbi "195                                       "\n").split()))196                except ValueError:197                    print("Please input number")198                    continue199                for index in indexes:200                    if 0 < index < 6:201                        select_parameters.fundict[index]()202                    else:203                        print("The number is not between 1 and 5!")204                break205        # if your own parameter data is input, parameters will be replace by the data. the priority is206        # single_parameter_list(use cai_ref=) > parameter_file > parameter_selected. when you try to change a single207        # parameter, for example using cai_ref, make it equal to a list or an array is both OK but not a dataframe.208        if additional_request:209            if parameter_file:210                select_parameters.process_parameter_file(parameter_file=parameter_file)211            if cai_ref:212                select_parameters.cai_reference_frequency = list(cai_ref)213            if tai_tGCN:214                select_parameters.tai_tGCN = list(tai_tGCN)215            if ite_heg:216                select_parameters.ite_heg = list(ite_heg)217            if ite_bg:218                select_parameters.ite_bg = list(ite_bg)219            if cbi_opt:220                select_parameters.cbi_opt_codon = list(cbi_opt)221            if fop_opt:222                select_parameters.fop_opt_codon = list(fop_opt)223        return224    @staticmethod225    def generate_parameter_file(sorting='TCAG'):226        # use different sorting method, the result xlsx file will have sample name of different sorting.227        # for example, "TCAG" for "TTT" to "GGG", "amino_acid" for "GCA" to "TGA".228        if sorting == 'TCAG':229            parameter_dataframe = pd.DataFrame(index=["TTT", "TTC", "TTA", "TTG",230                                                      "TCT", "TCC", "TCA", "TCG",231                                                      "TAT", "TAC", "TAA", "TAG",232                                                      "TGT", "TGC", "TGA", "TGG",233                                                      "CTT", "CTC", "CTA", "CTG",234                                                      "CCT", "CCC", "CCA", "CCG",235                                                      "CAT", "CAC", "CAA", "CAG",236                                                      "CGT", "CGC", "CGA", "CGG",237                                                      "ATT", "ATC", "ATA", "ATG",238                                                      "ACT", "ACC", "ACA", "ACG",239                                                      "AAT", "AAC", "AAA", "AAG",240                                                      "AGT", "AGC", "AGA", "AGG",241                                                      "GTT", "GTC", "GTA", "GTG",242                                                      "GCT", "GCC", "GCA", "GCG",243                                                      "GAT", "GAC", "GAA", "GAG",244                                                      "GGT", "GGC", "GGA", "GGG"])245        elif sorting == 'amino_acid':246            parameter_dataframe = pd.DataFrame(247                index=['GCA', 'GCC', 'GCG', 'GCT', 'TGC', 'TGT', 'GAC', 'GAT', 'GAA', 'GAG', 'TTC', 'TTT',248                       'GGA', 'GGC',249                       'GGG', 'GGT', 'CAC', 'CAT', 'ATA', 'ATC', 'ATT', 'AAA', 'AAG', 'CTA', 'CTC', 'CTG',250                       'CTT', 'TTA',251                       'TTG', 'ATG', 'AAC', 'AAT', 'CCA', 'CCC', 'CCG', 'CCT', 'CAA', 'CAG', 'AGA', 'AGG',252                       'CGA', 'CGC',253                       'CGG', 'CGT', 'AGC', 'AGT', 'TCA', 'TCC', 'TCG', 'TCT', 'ACA', 'ACC', 'ACG', 'ACT',254                       'GTA', 'GTC',255                       'GTG', 'GTT', 'TGG', 'TAC', 'TAT', 'TAA', 'TAG', 'TGA'])256        else:257            raise ValueError("Counldn't find the sorting method you use.")258        parameter_dataframe['cai_ref'] = select_parameters.cai_reference_frequency259        parameter_dataframe['tai_tGCN'] = select_parameters.tai_tGCN260        ite_dataframe = pd.DataFrame(index=["TTT", "TTC", "TTA", "TTG",261                                            "TCT", "TCC", "TCA", "TCG",262                                            "TAT", "TAC",263                                            "TGT", "TGC", "TGG",264                                            "CTT", "CTC", "CTA", "CTG",265                                            "CCT", "CCC", "CCA", "CCG",266                                            "CAT", "CAC", "CAA", "CAG",267                                            "CGT", "CGC", "CGA", "CGG",268                                            "ATT", "ATC", "ATA", "ATG",269                                            "ACT", "ACC", "ACA", "ACG",270                                            "AAT", "AAC", "AAA", "AAG",271                                            "AGT", "AGC", "AGA", "AGG",272                                            "GTT", "GTC", "GTA", "GTG",273                                            "GCT", "GCC", "GCA", "GCG",274                                            "GAT", "GAC", "GAA", "GAG",275                                            "GGT", "GGC", "GGA", "GGG"])276        ite_dataframe['ite_heg'] = select_parameters.ite_heg277        ite_dataframe['ite_bg'] = select_parameters.ite_bg278        parameter_dataframe = pd.concat([parameter_dataframe, ite_dataframe], axis=1)279        if select_parameters.cbi_opt_codon:280            parameter_dataframe['cbi_opt'] = select_parameters.cbi_opt_codon281        if select_parameters.fop_opt_codon:282            parameter_dataframe['fop_opt'] = select_parameters.fop_opt_codon283        parameter_dataframe.to_excel('parameter_file.xlsx')284        return285# c = select_parameters( parameter_file='parameter_file.xlsx', cai_ref=[1], skip_select=True)286# c.process_parameter_file()287# print(c.cai_reference_frequency)288# print(c.cbi_opt_codon)...

c.py

Source:c.py

1import streamlit as st2import time3import numpy as np4import pandas as pd5import matplotlib.pyplot as plt6import datetime7plt.rcParams['font.sans-serif']=['SimHei']8plt.rcParams['axes.unicode_minus'] = False9 10def sidebar():11    df_parameters = pd.DataFrame({'industry': ['-','éè','å»ç'],'name': ['-','A','B'],'time': ['-','5å¤©','7å¤©']})12    select_parameters=[]13    option_industry = '-'14    option_name = '-'15    option_time = '-'16    17    option_industry = st.sidebar.selectbox('è¯·éæ©è¡ç¥¨è¡ä¸',df_parameters['industry'])18    if option_industry != '-':19        select_parameters.append(option_industry)20        option_name = st.sidebar.selectbox('è¯·éæ©è¡ç¥¨åç§°',df_parameters['name'])21    else:22        pass23    24    if option_name!= '-':25        select_parameters.append(option_name)26        option_time = st.sidebar.selectbox('è¯·éæ©æ¶é´çªå£',df_parameters['time'])27    else:28        pass29        30    if option_time!= '-':31        select_parameters.append(option_time)32        version_option=st.sidebar.selectbox('éæ©åå²çæ¬ï¼',('-','çæ¬1', 'çæ¬2', 'çæ¬3'))33        st.sidebar.button('åæ¢é¡µé¢')34        st.sidebar.button('æ´æ°åæ°')35        36    return select_parameters37 38def display():39    st.set_option('deprecation.showPyplotGlobalUse', False)    40    #st.title(time.strftime("å½åæ¥æï¼%Yå¹´%mæ%dæ¥", time.localtime()))41    start_date = "20200101"42    gap = 36543    date_list = pd.to_datetime(pd.date_range(start=start_date, periods=gap).strftime("%Y%m%d").tolist())44    chart_data = pd.DataFrame(np.random.randn(365, 2),columns=['çå®å¼','é¢æµå¼'],index=date_list)#ä¼ å¥DataFrame45    plt.plot(date_list,chart_data['çå®å¼'],label='çå®å¼') 46    plt.plot(date_list,chart_data['é¢æµå¼'],label='é¢æµå¼')47    plt.ylim(bottom=0)48    plt.xticks(rotation=45)49    plt.title(sidebar[0]+'è¡ä¸'+sidebar[1]+'è¡ç¥¨å¨'+sidebar[2]+'æ¶é´çªå£ä¸çé¢æµç»æ',loc='center',fontproperties="SimHei",fontsize=20)50    plt.xlabel('æ¥æ',loc='right')51    plt.ylabel('ä»·æ ¼',loc='top')52    plt.legend(loc='upper right')53    st.pyplot()54    55def button():56    ini_date=datetime.date.today()57    end_date=datetime.date.today()58    st.write('è®ç»æ°æ®ï¼')59    ini_date=st.date_input('èµ·å§æ¥æï¼',value=datetime.date(2001,1,1))60    end_date=st.date_input('æªæ¢æ¥æï¼') 61    st.button('ç¡®å®')62    return ini_date,end_date63 64sidebar=sidebar()65#st.write(run)66if len(sidebar) != 3:67    st.header('è¯·éæ©åæ°ï¼')68else:69    display()
...

st_sidebar.py

Source:st_sidebar.py

1"""2@author raymanlei3@since 2022-05-08 22:27:254"""5import streamlit as st6import pandas as pd7from datetime import datetime as dt8def update_parameters(select_parameters):9    st.header('æ¨¡æ¿ï¼' + str(dt.now().__format__('%Y-%m-%d')) + ' åææ¥å by raymanlei')10    st.info(select_parameters)11    return12def sidebar():13    print('--->>> sidebar')14    # file uploader15    uploaded_files = st.sidebar.file_uploader("Upload your Python DA Script", type=["py"], accept_multiple_files=True)16    if uploaded_files is not None:17        for f in uploaded_files:18            input_df = pd.read_csv(f)19            print(input_df.head(5))20    # part 221    df_parameters = pd.DataFrame(22        {'industry': ['-', 'Financial', 'Medical'], 'name': ['-', 'Alibaba', 'Tencent'],23         'time': ['-', '5 Days', '7 Days']})24    select_parameters = []25    option_industry = '-'26    option_name = '-'27    option_time = '-'28    option_industry = st.sidebar.selectbox('Industry', df_parameters['industry'])29    if option_industry != '-':30        select_parameters.append(option_industry)31        option_name = st.sidebar.selectbox('Stock', df_parameters['name'])32    else:33        pass34    if option_name != '-':35        select_parameters.append(option_name)36        option_time = st.sidebar.selectbox('Times', df_parameters['time'])37    else:38        pass39    if option_time != '-':40        select_parameters.append(option_time)41        version_option = st.sidebar.selectbox('Anonymousï¼', ('-', 'Anonymous 1', 'Anonymous 2', 'Anonymous 3'))42        st.sidebar.button('Button Alpha')43        st.sidebar.button('Button Beta', on_click=update_parameters(select_parameters))...

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