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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 "'nested'" in text144 assert "<xarray.Dataset>" 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 "'nested'" 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 "<xarray.DataArray 'var1'" 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
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