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How to use set_options method in Testify

Best Python code snippet using Testify_python

Snakefile

Source:Snakefile

...92        M0_cml.alignment = input[1]93        M0_cml.tree = input[0]94        M0_cml.out_file = output[0]95        M0_cml.working_dir = output[0].split('/')[:-1][0] +'/'+output[0].split('/')[:-1][1]+'/'+output[0].split('/')[:-1][2]+'/'96        M0_cml.set_options(noisy = 9)	         # 0,1,2,3,9: how much rubbish on the screen97        M0_cml.set_options(verbose = 1)	     # 1: detailed output, 0: concise output98        M0_cml.set_options(runmode = 0)	     # 0: user tree;  1: semi-automatic;  2: automatic99        M0_cml.set_options(seqtype = 1)	     # 1:codons; 2:AAs; 3:codons-->AAs100        M0_cml.set_options(CodonFreq = 2)	     # 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table101        M0_cml.set_options(clock = 0)	         # 0: no clock, unrooted tree, 1: clock, rooted tree102        M0_cml.set_options(aaDist = 0)	         # 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}103        M0_cml.set_options(model = 0)	         # models for codons:104        M0_cml.set_options(NSsites = [0])	     # 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete; Needs to be array105        M0_cml.set_options(icode = 0)	         # 0:standard genetic code; 1:mammalian mt; 2-10:see below106        M0_cml.set_options(Mgene = 0)	         # 0:rates, 1:separate; 2:pi, 3:kappa, 4:all107        M0_cml.set_options(fix_kappa = 0)	     # 1: kappa fixed, 0: kappa to be estimated108        M0_cml.set_options(kappa = 2)	         # initial or fixed kappa109        M0_cml.set_options(fix_omega = 0)	     # 1: omega or omega_1 fixed, 0: estimate110        M0_cml.set_options(omega = 1)	         # initial or fixed omega, for codons or codon-based AAs111        M0_cml.set_options(getSE = 0)	         # 0: don't want them, 1: want S.E.s of estimates112        M0_cml.set_options(RateAncestor = 0)	 # (0,1,2): rates (alpha>0) or ancestral states (1 or 2)113        M0_cml.set_options(Small_Diff = .45e-6) # Default value.114        M0_cml.set_options(cleandata = 0)	     # remove sites with ambiguity data (1:yes, 0:no)?115        M0_cml.set_options(fix_blength = 0)	 # 0: ignore, -1: random, 1: initial, 2: fixed116        M0_results = M0_cml.run(verbose=True)117        identifiers = re.search("Families/"+"(.*)"+"_dir",output[0])118        family=identifiers.groups()[0]119        try:120            M0_lnL = M0_results.get("NSsites").get(0).get("lnL")121            M0_np = len(M0_results.get("NSsites").get(0).get("parameters").get("parameter list").split())122            # print("@@@@@@@@@@@@@@@@@@@@")123            # print(M0_lnL,M0_np,M0_cml.working_dir)124            with open(M0_cml.working_dir+"statsfile.txt","w") as out:125                out.write(family+"\tM0\t"+str(M0_np)+"\t"+str(M0_lnL)+"\n")126        except:127            with open(M0_cml.working_dir+"statsfile.txt","w") as out:128                out.write(family+"\tM0\tNA\tNA\n")129rule M1:130    input:131        "Families/family_{fam}_dir/M1/family_{fam}.tree",132        "Families/family_{fam}_dir/M1/family_{fam}.codon.phylip"133    output:134        "Families/family_{fam}_dir/M1/family_{fam}.mcl"135    run:136        COUNTER.append(True)137        M1_cml = codeml.Codeml()138        M1_cml.alignment = input[1]139        M1_cml.tree = input[0]140        M1_cml.out_file = output[0]141        M1_cml.working_dir = output[0].split('/')[:-1][0] +'/'+output[0].split('/')[:-1][1]+'/'+output[0].split('/')[:-1][2]+'/'142        M1_cml.set_options(noisy = 9)	         # 0,1,2,3,9: how much rubbish on the screen143        M1_cml.set_options(verbose = 1)	     # 1: detailed output, 0: concise output144        M1_cml.set_options(runmode = 0)	     # 0: user tree;  1: semi-automatic;  2: automatic145        M1_cml.set_options(seqtype = 1)	     # 1:codons; 2:AAs; 3:codons-->AAs146        M1_cml.set_options(CodonFreq = 2)	     # 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table147        M1_cml.set_options(clock = 0)	         # 0: no clock, unrooted tree, 1: clock, rooted tree148        M1_cml.set_options(aaDist = 0)	         # 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}149        M1_cml.set_options(model = 0)	         # models for codons:150        M1_cml.set_options(NSsites = [1])	     # 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete; Needs to be array151        M1_cml.set_options(icode = 0)	         # 0:standard genetic code; 1:mammalian mt; 2-10:see below152        M1_cml.set_options(Mgene = 0)	         # 0:rates, 1:separate; 2:pi, 3:kappa, 4:all153        M1_cml.set_options(fix_kappa = 0)	     # 1: kappa fixed, 0: kappa to be estimated154        M1_cml.set_options(kappa = 2)	         # initial or fixed kappa155        M1_cml.set_options(fix_omega = 0)	     # 1: omega or omega_1 fixed, 0: estimate156        M1_cml.set_options(omega = 1)	         # initial or fixed omega, for codons or codon-based AAs157        M1_cml.set_options(getSE = 0)	         # 0: don't want them, 1: want S.E.s of estimates158        M1_cml.set_options(RateAncestor = 0)	 # (0,1,2): rates (alpha>0) or ancestral states (1 or 2)159        M1_cml.set_options(Small_Diff = .45e-6) # Default value.160        M1_cml.set_options(cleandata = 0)	     # remove sites with ambiguity data (1:yes, 0:no)?161        M1_cml.set_options(fix_blength = 0)	 # 0: ignore, -1: random, 1: initial, 2: fixed162        M1_results = M1_cml.run(verbose=True)163        identifiers = re.search("Families/"+"(.*)"+"_dir",output[0])164        family=identifiers.groups()[0]165        try:166            M1_lnL = M1_results.get("NSsites").get(1).get("lnL")167            M1_np = len(M1_results.get("NSsites").get(1).get("parameters").get("parameter list").split())168            # print("@@@@@@@@@@@@@@@@@@@@")169            print(M1_lnL,M1_np,M1_cml.working_dir)170            with open(M1_cml.working_dir+"statsfile.txt","w") as out:171                out.write(family+"\tM1\t"+str(M1_np)+"\t"+str(M1_lnL)+"\n")172        except:173            with open(M1_cml.working_dir+"statsfile.txt","w") as out:174                out.write(family+"\tM1\tNA\tNA\n")175rule M2:176    input:177        "Families/family_{fam}_dir/M2/family_{fam}.tree",178        "Families/family_{fam}_dir/M2/family_{fam}.codon.phylip"179    output:180        "Families/family_{fam}_dir/M2/family_{fam}.mcl"181    run:182        COUNTER.append(True)183        M2_cml = codeml.Codeml()184        M2_cml.alignment = input[1]185        M2_cml.tree = input[0]186        M2_cml.out_file = output[0]187        M2_cml.working_dir = output[0].split('/')[:-1][0] +'/'+output[0].split('/')[:-1][1]+'/'+output[0].split('/')[:-1][2]+'/'188        M2_cml.set_options(noisy = 9)	         # 0,1,2,3,9: how much rubbish on the screen189        M2_cml.set_options(verbose = 1)	     # 1: detailed output, 0: concise output190        M2_cml.set_options(runmode = 0)	     # 0: user tree;  1: semi-automatic;  2: automatic191        M2_cml.set_options(seqtype = 1)	     # 1:codons; 2:AAs; 3:codons-->AAs192        M2_cml.set_options(CodonFreq = 2)	     # 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table193        M2_cml.set_options(clock = 0)	         # 0: no clock, unrooted tree, 1: clock, rooted tree194        M2_cml.set_options(aaDist = 0)	         # 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}195        M2_cml.set_options(model = 0)	         # models for codons:196        M2_cml.set_options(NSsites = [2])	     # 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete; Needs to be array197        M2_cml.set_options(icode = 0)	         # 0:standard genetic code; 1:mammalian mt; 2-10:see below198        M2_cml.set_options(Mgene = 0)	         # 0:rates, 1:separate; 2:pi, 3:kappa, 4:all199        M2_cml.set_options(fix_kappa = 0)	     # 1: kappa fixed, 0: kappa to be estimated200        M2_cml.set_options(kappa = 2)	         # initial or fixed kappa201        M2_cml.set_options(fix_omega = 0)	     # 1: omega or omega_1 fixed, 0: estimate202        M2_cml.set_options(omega = 1)	         # initial or fixed omega, for codons or codon-based AAs203        M2_cml.set_options(getSE = 0)	         # 0: don't want them, 1: want S.E.s of estimates204        M2_cml.set_options(RateAncestor = 0)	 # (0,1,2): rates (alpha>0) or ancestral states (1 or 2)205        M2_cml.set_options(Small_Diff = .45e-6) # Default value.206        M2_cml.set_options(cleandata = 0)	     # remove sites with ambiguity data (1:yes, 0:no)?207        M2_cml.set_options(fix_blength = 0)	 # 0: ignore, -1: random, 1: initial, 2: fixed208        M2_results = M2_cml.run(verbose=True)209        identifiers = re.search("Families/"+"(.*)"+"_dir",output[0])210        family=identifiers.groups()[0]211        try:212            M2_lnL = M2_results.get("NSsites").get(2).get("lnL")213            M2_np = len(M2_results.get("NSsites").get(2).get("parameters").get("parameter list").split())214            # print("@@@@@@@@@@@@@@@@@@@@")215            # print(M2_lnL,M2_np,M2_cml.working_dir)216            with open(M2_cml.working_dir+"statsfile.txt","w") as out:217                out.write(family+"\tM2\t"+str(M2_np)+"\t"+str(M2_lnL)+"\n")218        except:219            with open(M2_cml.working_dir+"statsfile.txt","w") as out:220                out.write(family+"\tM2\tNA\tNA\n")221rule M3:222    input:223        "Families/family_{fam}_dir/M3/family_{fam}.tree",224        "Families/family_{fam}_dir/M3/family_{fam}.codon.phylip"225    output:226        "Families/family_{fam}_dir/M3/family_{fam}.mcl"227    run:228        COUNTER.append(True)229        M3_cml = codeml.Codeml()230        M3_cml.alignment = input[1]231        M3_cml.tree = input[0]232        M3_cml.out_file = output[0]233        M3_cml.working_dir = output[0].split('/')[:-1][0] +'/'+output[0].split('/')[:-1][1]+'/'+output[0].split('/')[:-1][2]+'/'234        M3_cml.set_options(noisy = 9)	         # 0,1,2,3,9: how much rubbish on the screen235        M3_cml.set_options(verbose = 1)	     # 1: detailed output, 0: concise output236        M3_cml.set_options(runmode = 0)	     # 0: user tree;  1: semi-automatic;  2: automatic237        M3_cml.set_options(seqtype = 1)	     # 1:codons; 2:AAs; 3:codons-->AAs238        M3_cml.set_options(CodonFreq = 2)	     # 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table239        M3_cml.set_options(clock = 0)	         # 0: no clock, unrooted tree, 1: clock, rooted tree240        M3_cml.set_options(aaDist = 0)	         # 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}241        M3_cml.set_options(model = 0)	         # models for codons:242        M3_cml.set_options(NSsites = [3])	     # 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete; Needs to be array243        M3_cml.set_options(icode = 0)	         # 0:standard genetic code; 1:mammalian mt; 2-10:see below244        M3_cml.set_options(Mgene = 0)	         # 0:rates, 1:separate; 2:pi, 3:kappa, 4:all245        M3_cml.set_options(fix_kappa = 0)	     # 1: kappa fixed, 0: kappa to be estimated246        M3_cml.set_options(kappa = 2)	         # initial or fixed kappa247        M3_cml.set_options(fix_omega = 0)	     # 1: omega or omega_1 fixed, 0: estimate248        M3_cml.set_options(omega = 1)	         # initial or fixed omega, for codons or codon-based AAs249        M3_cml.set_options(getSE = 0)	         # 0: don't want them, 1: want S.E.s of estimates250        M3_cml.set_options(RateAncestor = 0)	 # (0,1,2): rates (alpha>0) or ancestral states (1 or 2)251        M3_cml.set_options(Small_Diff = .45e-6) # Default value.252        M3_cml.set_options(cleandata = 0)	     # remove sites with ambiguity data (1:yes, 0:no)?253        M3_cml.set_options(fix_blength = 0)	 # 0: ignore, -1: random, 1: initial, 2: fixed254        M3_results = M3_cml.run(verbose=True)255        identifiers = re.search("Families/"+"(.*)"+"_dir",output[0])256        family=identifiers.groups()[0]257        try:258            M3_lnL = M3_results.get("NSsites").get(3).get("lnL")259            M3_np = len(M3_results.get("NSsites").get(3).get("parameters").get("parameter list").split())260            # print("@@@@@@@@@@@@@@@@@@@@")261            # print(M3_lnL,M3_np,M3_cml.working_dir)262            with open(M3_cml.working_dir+"statsfile.txt","w") as out:263                out.write(family+"\tM3\t"+str(M3_np)+"\t"+str(M3_lnL)+"\n")264        except:265            with open(M3_cml.working_dir+"statsfile.txt","w") as out:266                out.write(family+"\tM3\tNA\tNA\n")267rule M7:268    input:269        "Families/family_{fam}_dir/M7/family_{fam}.tree",270        "Families/family_{fam}_dir/M7/family_{fam}.codon.phylip"271    output:272        "Families/family_{fam}_dir/M7/family_{fam}.mcl"273    run:274        COUNTER.append(True)275        M7_cml = codeml.Codeml()276        M7_cml.alignment = input[1]277        M7_cml.tree = input[0]278        M7_cml.out_file = output[0]279        M7_cml.working_dir = output[0].split('/')[:-1][0] +'/'+output[0].split('/')[:-1][1]+'/'+output[0].split('/')[:-1][2]+'/'280        M7_cml.set_options(noisy = 9)	         # 0,1,2,3,9: how much rubbish on the screen281        M7_cml.set_options(verbose = 1)	     # 1: detailed output, 0: concise output282        M7_cml.set_options(runmode = 0)	     # 0: user tree;  1: semi-automatic;  2: automatic283        M7_cml.set_options(seqtype = 1)	     # 1:codons; 2:AAs; 3:codons-->AAs284        M7_cml.set_options(CodonFreq = 2)	     # 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table285        M7_cml.set_options(clock = 0)	         # 0: no clock, unrooted tree, 1: clock, rooted tree286        M7_cml.set_options(aaDist = 0)	         # 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}287        M7_cml.set_options(model = 0)	         # models for codons:288        M7_cml.set_options(NSsites = [7])	     # 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete; Needs to be array289        M7_cml.set_options(icode = 0)	         # 0:standard genetic code; 1:mammalian mt; 2-10:see below290        M7_cml.set_options(Mgene = 0)	         # 0:rates, 1:separate; 2:pi, 3:kappa, 4:all291        M7_cml.set_options(fix_kappa = 0)	     # 1: kappa fixed, 0: kappa to be estimated292        M7_cml.set_options(kappa = 2)	         # initial or fixed kappa293        M7_cml.set_options(fix_omega = 0)	     # 1: omega or omega_1 fixed, 0: estimate294        M7_cml.set_options(omega = 1)	         # initial or fixed omega, for codons or codon-based AAs295        M7_cml.set_options(getSE = 0)	         # 0: don't want them, 1: want S.E.s of estimates296        M7_cml.set_options(RateAncestor = 0)	 # (0,1,2): rates (alpha>0) or ancestral states (1 or 2)297        M7_cml.set_options(Small_Diff = .45e-6) # Default value.298        M7_cml.set_options(cleandata = 0)	     # remove sites with ambiguity data (1:yes, 0:no)?299        M7_cml.set_options(fix_blength = 0)	 # 0: ignore, -1: random, 1: initial, 2: fixed300        M7_results = M7_cml.run(verbose=True)301        identifiers = re.search("Families/"+"(.*)"+"_dir",output[0])302        family=identifiers.groups()[0]303        try:304            M7_lnL = M7_results.get("NSsites").get(7).get("lnL")305            M7_np = len(M7_results.get("NSsites").get(7).get("parameters").get("parameter list").split())306            # print("@@@@@@@@@@@@@@@@@@@@")307            # print(M7_lnL,M7_np,M7_cml.working_dir)308            with open(M7_cml.working_dir+"statsfile.txt","w") as out:309                out.write(family+"\tM7\t"+str(M7_np)+"\t"+str(M7_lnL)+"\n")310        except:311            with open(M7_cml.working_dir+"statsfile.txt","w") as out:312                out.write(family+"\tM7\tNA\tNA\n")313rule M8:314    input:315        "Families/family_{fam}_dir/M8/family_{fam}.tree",316        "Families/family_{fam}_dir/M8/family_{fam}.codon.phylip"317    output:318        "Families/family_{fam}_dir/M8/family_{fam}.mcl"319    run:320        COUNTER.append(True)321        M8_cml = codeml.Codeml()322        M8_cml.alignment = input[1]323        M8_cml.tree = input[0]324        M8_cml.out_file = output[0]325        M8_cml.working_dir = output[0].split('/')[:-1][0] +'/'+output[0].split('/')[:-1][1]+'/'+output[0].split('/')[:-1][2]+'/'326        M8_cml.set_options(noisy = 9)	         # 0,1,2,3,9: how much rubbish on the screen327        M8_cml.set_options(verbose = 1)	     # 1: detailed output, 0: concise output328        M8_cml.set_options(runmode = 0)	     # 0: user tree;  1: semi-automatic;  2: automatic329        M8_cml.set_options(seqtype = 1)	     # 1:codons; 2:AAs; 3:codons-->AAs330        M8_cml.set_options(CodonFreq = 2)	     # 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table331        M8_cml.set_options(clock = 0)	         # 0: no clock, unrooted tree, 1: clock, rooted tree332        M8_cml.set_options(aaDist = 0)	         # 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}333        M8_cml.set_options(model = 0)	         # models for codons:334        M8_cml.set_options(NSsites = [8])	     # 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete; Needs to be array335        M8_cml.set_options(icode = 0)	         # 0:standard genetic code; 1:mammalian mt; 2-10:see below336        M8_cml.set_options(Mgene = 0)	         # 0:rates, 1:separate; 2:pi, 3:kappa, 4:all337        M8_cml.set_options(fix_kappa = 0)	     # 1: kappa fixed, 0: kappa to be estimated338        M8_cml.set_options(kappa = 2)	         # initial or fixed kappa339        M8_cml.set_options(fix_omega = 0)	     # 1: omega or omega_1 fixed, 0: estimate340        M8_cml.set_options(omega = 1)	         # initial or fixed omega, for codons or codon-based AAs341        M8_cml.set_options(getSE = 0)	         # 0: don't want them, 1: want S.E.s of estimates342        M8_cml.set_options(RateAncestor = 0)	 # (0,1,2): rates (alpha>0) or ancestral states (1 or 2)343        M8_cml.set_options(Small_Diff = .45e-6) # Default value.344        M8_cml.set_options(cleandata = 0)	     # remove sites with ambiguity data (1:yes, 0:no)?345        M8_cml.set_options(fix_blength = 0)	 # 0: ignore, -1: random, 1: initial, 2: fixed346        M8_results = M8_cml.run(verbose=True)347        identifiers = re.search("Families/"+"(.*)"+"_dir",output[0])348        family=identifiers.groups()[0]349        try:350            M8_lnL = M8_results.get("NSsites").get(8).get("lnL")351            M8_np = len(M8_results.get("NSsites").get(8).get("parameters").get("parameter list").split())352            # print("@@@@@@@@@@@@@@@@@@@@")353            # print(M8_lnL,M8_np,M8_cml.working_dir)354            with open(M8_cml.working_dir+"statsfile.txt","w") as out:355                out.write(family+"\tM8\t"+str(M8_np)+"\t"+str(M8_lnL)+"\n")356        except:357            with open(M8_cml.working_dir+"statsfile.txt","w") as out:358                out.write(family+"\tM8\tNA\tNA\n")359rule M8a:360    input:361        "Families/family_{fam}_dir/M8a/family_{fam}.tree",362        "Families/family_{fam}_dir/M8a/family_{fam}.codon.phylip"363    output:364        "Families/family_{fam}_dir/M8a/family_{fam}.mcl"365    run:366        COUNTER.append(True)367        M8a_cml = codeml.Codeml()368        M8a_cml.alignment = input[1]369        M8a_cml.tree = input[0]370        M8a_cml.out_file = output[0]371        M8a_cml.working_dir = output[0].split('/')[:-1][0] +'/'+output[0].split('/')[:-1][1]+'/'+output[0].split('/')[:-1][2]+'/'372        M8a_cml.set_options(noisy = 9)	         # 0,1,2,3,9: how much rubbish on the screen373        M8a_cml.set_options(verbose = 1)	     # 1: detailed output, 0: concise output374        M8a_cml.set_options(runmode = 0)	     # 0: user tree;  1: semi-automatic;  2: automatic375        M8a_cml.set_options(seqtype = 1)	     # 1:codons; 2:AAs; 3:codons-->AAs376        M8a_cml.set_options(CodonFreq = 2)	     # 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table377        M8a_cml.set_options(clock = 0)	         # 0: no clock, unrooted tree, 1: clock, rooted tree378        M8a_cml.set_options(aaDist = 0)	         # 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}379        M8a_cml.set_options(model = 0)	         # models for codons:380        M8a_cml.set_options(NSsites = [8])	     # 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete; Needs to be array381        M8a_cml.set_options(icode = 0)	         # 0:standard genetic code; 1:mammalian mt; 2-10:see below382        M8a_cml.set_options(Mgene = 0)	         # 0:rates, 1:separate; 2:pi, 3:kappa, 4:all383        M8a_cml.set_options(fix_kappa = 0)	     # 1: kappa fixed, 0: kappa to be estimated384        M8a_cml.set_options(kappa = 2)	         # initial or fixed kappa385        M8a_cml.set_options(fix_omega = 1)	     # 1: omega or omega_1 fixed, 0: estimate386        M8a_cml.set_options(omega = 1)	         # initial or fixed omega, for codons or codon-based AAs387        M8a_cml.set_options(getSE = 0)	         # 0: don't want them, 1: want S.E.s of estimates388        M8a_cml.set_options(RateAncestor = 0)	 # (0,1,2): rates (alpha>0) or ancestral states (1 or 2)389        M8a_cml.set_options(Small_Diff = .45e-6) # Default value.390        M8a_cml.set_options(cleandata = 0)	     # remove sites with ambiguity data (1:yes, 0:no)?391        M8a_cml.set_options(fix_blength = 0)	 # 0: ignore, -1: random, 1: initial, 2: fixed392        M8a_results=M8a_cml.run(verbose=True)393        identifiers = re.search("Families/"+"(.*)"+"_dir",output[0])394        family=identifiers.groups()[0]395        try:396            M8a_lnL = M8a_results.get("NSsites").get(8).get("lnL")397            M8a_np = len(M8a_results.get("NSsites").get(8).get("parameters").get("parameter list").split())398            # print("@@@@@@@@@@@@@@@@@@@@")399            # print(M8a_lnL,M8a_np,M8a_cml.working_dir+"statsfile.txt")400            with open(M8a_cml.working_dir+"statsfile.txt","w") as out:401                out.write(family+"\tM8a\t"+str(M8a_np)+"\t"+str(M8a_lnL)+"\n")402        except:403            with open(M8a_cml.working_dir+"statsfile.txt","w") as out:404                out.write(family+"\tM8a\tNA\tNA\n")405rule codemlModels:...

test_options.py

Source:test_options.py

...5from xarray.core.options import OPTIONS, _get_keep_attrs6from xarray.tests.test_dataset import create_test_data7def test_invalid_option_raises():8    with pytest.raises(ValueError):9        xarray.set_options(not_a_valid_options=True)10def test_display_width():11    with pytest.raises(ValueError):12        xarray.set_options(display_width=0)13    with pytest.raises(ValueError):14        xarray.set_options(display_width=-10)15    with pytest.raises(ValueError):16        xarray.set_options(display_width=3.5)17def test_arithmetic_join():18    with pytest.raises(ValueError):19        xarray.set_options(arithmetic_join="invalid")20    with xarray.set_options(arithmetic_join="exact"):21        assert OPTIONS["arithmetic_join"] == "exact"22def test_enable_cftimeindex():23    with pytest.raises(ValueError):24        xarray.set_options(enable_cftimeindex=None)25    with pytest.warns(FutureWarning, match="no-op"):26        with xarray.set_options(enable_cftimeindex=True):27            assert OPTIONS["enable_cftimeindex"]28def test_file_cache_maxsize():29    with pytest.raises(ValueError):30        xarray.set_options(file_cache_maxsize=0)31    original_size = FILE_CACHE.maxsize32    with xarray.set_options(file_cache_maxsize=123):33        assert FILE_CACHE.maxsize == 12334    assert FILE_CACHE.maxsize == original_size35def test_keep_attrs():36    with pytest.raises(ValueError):37        xarray.set_options(keep_attrs="invalid_str")38    with xarray.set_options(keep_attrs=True):39        assert OPTIONS["keep_attrs"]40    with xarray.set_options(keep_attrs=False):41        assert not OPTIONS["keep_attrs"]42    with xarray.set_options(keep_attrs="default"):43        assert _get_keep_attrs(default=True)44        assert not _get_keep_attrs(default=False)45def test_nested_options():46    original = OPTIONS["display_width"]47    with xarray.set_options(display_width=1):48        assert OPTIONS["display_width"] == 149        with xarray.set_options(display_width=2):50            assert OPTIONS["display_width"] == 251        assert OPTIONS["display_width"] == 152    assert OPTIONS["display_width"] == original53def test_display_style():54    original = "text"55    assert OPTIONS["display_style"] == original56    with pytest.raises(ValueError):57        xarray.set_options(display_style="invalid_str")58    with xarray.set_options(display_style="html"):59        assert OPTIONS["display_style"] == "html"60    assert OPTIONS["display_style"] == original61def create_test_dataset_attrs(seed=0):62    ds = create_test_data(seed)63    ds.attrs = {"attr1": 5, "attr2": "history", "attr3": {"nested": "more_info"}}64    return ds65def create_test_dataarray_attrs(seed=0, var="var1"):66    da = create_test_data(seed)[var]67    da.attrs = {"attr1": 5, "attr2": "history", "attr3": {"nested": "more_info"}}68    return da69class TestAttrRetention:70    def test_dataset_attr_retention(self):71        # Use .mean() for all tests: a typical reduction operation72        ds = create_test_dataset_attrs()73        original_attrs = ds.attrs74        # Test default behaviour75        result = ds.mean()76        assert result.attrs == {}77        with xarray.set_options(keep_attrs="default"):78            result = ds.mean()79            assert result.attrs == {}80        with xarray.set_options(keep_attrs=True):81            result = ds.mean()82            assert result.attrs == original_attrs83        with xarray.set_options(keep_attrs=False):84            result = ds.mean()85            assert result.attrs == {}86    def test_dataarray_attr_retention(self):87        # Use .mean() for all tests: a typical reduction operation88        da = create_test_dataarray_attrs()89        original_attrs = da.attrs90        # Test default behaviour91        result = da.mean()92        assert result.attrs == {}93        with xarray.set_options(keep_attrs="default"):94            result = da.mean()95            assert result.attrs == {}96        with xarray.set_options(keep_attrs=True):97            result = da.mean()98            assert result.attrs == original_attrs99        with xarray.set_options(keep_attrs=False):100            result = da.mean()101            assert result.attrs == {}102    def test_groupby_attr_retention(self):103        da = xarray.DataArray([1, 2, 3], [("x", [1, 1, 2])])104        da.attrs = {"attr1": 5, "attr2": "history", "attr3": {"nested": "more_info"}}105        original_attrs = da.attrs106        # Test default behaviour107        result = da.groupby("x").sum(keep_attrs=True)108        assert result.attrs == original_attrs109        with xarray.set_options(keep_attrs="default"):110            result = da.groupby("x").sum(keep_attrs=True)111            assert result.attrs == original_attrs112        with xarray.set_options(keep_attrs=True):113            result1 = da.groupby("x")114            result = result1.sum()115            assert result.attrs == original_attrs116        with xarray.set_options(keep_attrs=False):117            result = da.groupby("x").sum()118            assert result.attrs == {}119    def test_concat_attr_retention(self):120        ds1 = create_test_dataset_attrs()121        ds2 = create_test_dataset_attrs()122        ds2.attrs = {"wrong": "attributes"}123        original_attrs = ds1.attrs124        # Test default behaviour of keeping the attrs of the first125        # dataset in the supplied list126        # global keep_attrs option current doesn't affect concat127        result = concat([ds1, ds2], dim="dim1")128        assert result.attrs == original_attrs129    @pytest.mark.xfail130    def test_merge_attr_retention(self):131        da1 = create_test_dataarray_attrs(var="var1")132        da2 = create_test_dataarray_attrs(var="var2")133        da2.attrs = {"wrong": "attributes"}134        original_attrs = da1.attrs135        # merge currently discards attrs, and the global keep_attrs136        # option doesn't affect this137        result = merge([da1, da2])138        assert result.attrs == original_attrs139    def test_display_style_text(self):140        ds = create_test_dataset_attrs()141        text = ds._repr_html_()142        assert text.startswith("<pre>")143        assert "&#x27;nested&#x27;" in text144        assert "&lt;xarray.Dataset&gt;" in text145    def test_display_style_html(self):146        ds = create_test_dataset_attrs()147        with xarray.set_options(display_style="html"):148            html = ds._repr_html_()149            assert html.startswith("<div>")150            assert "&#x27;nested&#x27;" in html151    def test_display_dataarray_style_text(self):152        da = create_test_dataarray_attrs()153        text = da._repr_html_()154        assert text.startswith("<pre>")155        assert "&lt;xarray.DataArray &#x27;var1&#x27;" in text156    def test_display_dataarray_style_html(self):157        da = create_test_dataarray_attrs()158        with xarray.set_options(display_style="html"):159            html = da._repr_html_()160            assert html.startswith("<div>")...

PAML_codeml_dnds.py

Source:PAML_codeml_dnds.py

...3334                cml = codeml.Codeml(alignment = alignment, tree = tree,35                    out_file = out_file1, working_dir = dataDir)3637                cml.set_options(verbose=1)38                cml.set_options(CodonFreq=2)39                cml.set_options(cleandata=0)40                cml.set_options(fix_blength=0)41                cml.set_options(model=1)42                cml.set_options(NSsites=[0])43                cml.set_options(fix_omega=0)44                cml.set_options(omega=0)45                cml.set_options(clock=0)46                cml.set_options(ncatG=10)47                cml.set_options(runmode=0)48                cml.set_options(fix_kappa=0)49                cml.set_options(kappa=0.4)50                cml.set_options(fix_alpha=1)51                cml.set_options(alpha=0)52                cml.set_options(Malpha=0)53                cml.set_options(Small_Diff=1e-6)54                cml.set_options(method=1)55                cml.set_options(aaDist=0)56                cml.set_options(RateAncestor=0)57                cml.set_options(aaRatefile="wag.dat")58                cml.set_options(icode=0)59                cml.set_options(seqtype=1)60                cml.set_options(getSE=0)61                cml.set_options(noisy=9)62                cml.set_options(Mgene=0)63               64                run1 = cml.run(command="C:\\Program Files (x86)\\paml4.9j\\bin\\codeml",verbose = True)65    66                results = run1.get('NSsites')67                results_1 = results.get(0)68                w_branch = results_1.get('omega tree')69                w = results_1.get('parameters').get('omega')70                PP = w[4]71                PV = w[5]7273                writer.writerow([gene,w_branch,PP,PV])7475            except:76                pass77            78# For single file7980outputfile = os.path.join(outDir, "all_loci_PAML.csv")81with open(outputfile, 'wb') as f:82    writer = csv.writer(f, delimiter=',')83    writer.writerow(["Gene","Omega_for_each_branch","Downy","Hairy"])84    85    filename= "all_loci.phy"86    filepath= "all_loci.phy"87    gene = filename.split(".")[0]88    alignment = filepath89    out_file1 = os.path.join(outDir,gene+"_model1.results")90    91    try:92        # model 1 (Null Model)9394        cml = codeml.Codeml(alignment = alignment, tree = tree,95            out_file = out_file1, working_dir = dataDir)9697        cml.set_options(verbose=1)98        cml.set_options(CodonFreq=2)99        cml.set_options(cleandata=0)100        cml.set_options(fix_blength=0)101        cml.set_options(model=1)102        cml.set_options(NSsites=[0])103        cml.set_options(fix_omega=0)104        cml.set_options(omega=0)105        cml.set_options(clock=0)106        cml.set_options(ncatG=10)107        cml.set_options(runmode=0)108        cml.set_options(fix_kappa=0)109        cml.set_options(kappa=0.4)110        cml.set_options(fix_alpha=1)111        cml.set_options(alpha=0)112        cml.set_options(Malpha=0)113        cml.set_options(Small_Diff=1e-6)114        cml.set_options(method=1)115        cml.set_options(aaDist=0)116        cml.set_options(RateAncestor=0)117        cml.set_options(aaRatefile="wag.dat")118        cml.set_options(icode=0)119        cml.set_options(seqtype=1)120        cml.set_options(getSE=0)121        cml.set_options(noisy=9)122        cml.set_options(Mgene=0)123       124        run1 = cml.run(command="C:\\Program Files (x86)\\paml4.9j\\bin\\codeml",verbose = True)125126        results = run1.get('NSsites')127        results_1 = results.get(0)128        w_branch = results_1.get('omega tree')129        w = results_1.get('parameters').get('omega')130        PP = w[4]131        PV = w[5]132133        writer.writerow([gene,w_branch,PP,PV])134135    except:136        pass

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.