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Commit 9f20b4c1 by Mustafa Tekpinar
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Removed some commented lines in launchJET function in gemmeAnal.py.

parent 96910f1f
Showing with 0 additions and 149 deletions
gemmeAnal.py
...@@ -197,155 +197,6 @@ def launchJET(prot, retMet, bFile, fFile, pdbfile, chains, n, N, nl): ...@@ -197,155 +197,6 @@ def launchJET(prot, retMet, bFile, fFile, pdbfile, chains, n, N, nl):
if os.path.isfile(f_path): if os.path.isfile(f_path):
os.remove(f_path) os.remove(f_path)
os.rmdir(dir_name) os.rmdir(dir_name)
# # Calculate SC2
# jetcmd = "java -Xmx4096m -cp $JET2_PATH:$JET2_PATH/jet/extLibs/vecmath.jar jet.JET -c default.conf -i "+\
# prot+".pdb -o `pwd` -p AVJCG -r input -f "+prot+"_"+chainID+".fasta -d chain -n "+n+" -a 4"+" > "+prot+".out"
# #One can also add: -g 'trace,pc,cv,clusters,axs'
# print("\nRunning for SC2:\n"+jetcmd)
# reCode=subprocess.call(jetcmd,shell=True)
# if os.path.isfile(prot+"/"+prot+"_jet.res"):
# os.rename(prot+"/"+prot+"_jet.res",prot+"_jet_sc2.res")
# dir_name = prot+"/"
# if os.path.isdir(dir_name):
# for f in os.listdir(dir_name):
# f_path = os.path.join(dir_name, f)
# if os.path.isfile(f_path):
# os.remove(f_path)
# os.rmdir(dir_name)
# # Calculate SC3
# jetcmd = "java -Xmx4096m -cp $JET2_PATH:$JET2_PATH/jet/extLibs/vecmath.jar jet.JET -c default.conf -i "+\
# prot+".pdb -o `pwd` -p AVJCG -r input -f "+prot+"_"+chainID+".fasta -d chain -n "+n+" -a 5"+" > "+prot+".out"
# #One can also add: -g 'trace,pc,cv,clusters,axs'
# print("\nRunning for SC3:\n"+jetcmd)
# reCode=subprocess.call(jetcmd,shell=True)
# if os.path.isfile(prot+"/"+prot+"_jet.res"):
# os.rename(prot+"/"+prot+"_jet.res",prot+"_jet_sc3.res")
# dir_name = prot+"/"
# if os.path.isdir(dir_name):
# for f in os.listdir(dir_name):
# f_path = os.path.join(dir_name, f)
# if os.path.isfile(f_path):
# os.remove(f_path)
# os.rmdir(dir_name)
# dfSC1 = pd.read_table(prot+"_jet_sc1.res", delimiter=r"\s+")
# print(dfSC1)
# # dfSC2 = pd.read_table(prot+"_jet_sc2.res", delimiter=r"\s+")
# # print(dfSC2)
# # dfSC3 = pd.read_table(prot+"_jet_sc3.res", delimiter=r"\s+")
# # print(dfSC3)
# dfSC1['sc1'] =dfSC1['clusters']
# # dfSC1['sc2'] =dfSC2['clusters']
# # dfSC1['sc3'] =dfSC3['clusters']
# print(dfSC1)
# #Calculate combined values of SC1, SC2 and SC3 and normalize them.
# combinedValuesV1 = []
# combinedValuesV2 = []
# value1List = []
# value2List = []
# value3List = []
# value4List = []
# value5List = []
# value6List = []
# temp1List = []
# temp2List = []
# temp3List = []
# for i in range(len(dfSC1)):
# value1=(dfSC1.loc[i, "sc1"]*dfSC1.loc[i, "trace"]) + dfSC1.loc[i, "trace"]
# value1List.append(value1)
# value2=(dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)**2 + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)
# value2List.append(value2)
# value3=(dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)**2 + ((dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)
# value3List.append(value3)
# value4=(dfSC1.loc[i, "sc1"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)**2 + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
# value4List.append(value4)
# value5=(dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
# value5List.append(value5)
# value6=(dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
# value6List.append(value6)
# temp1 = (dfSC1.loc[i, "sc1"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)**2
# temp1List.append(temp1)
# temp2 = (dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)**2
# temp2List.append(temp2)
# temp3 = (dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)**2
# temp3List.append(temp3)
# combinedValuesV1.append(max([value1, value2, value3]))
# combinedValuesV2.append(max([value2, value3, value4]))
# #MinMax normalize the list
# combinedValuesV1MinMaxed = minMaxNormalization(combinedValuesV1)
# combinedValuesV2MinMaxed = minMaxNormalization(combinedValuesV2)
# # value1ListMinMaxed = minMaxNormalization(value1List)
# # value2ListMinMaxed = minMaxNormalization(value2List)
# # value3ListMinMaxed = minMaxNormalization(value3List)
# # value4ListMinMaxed = minMaxNormalization(value4List)
# # combinedValuesV3MinMaxed = []
# # for i in range(len(dfSC1)):
# # combinedValuesV3MinMaxed.append(max([value2ListMinMaxed[i], value3ListMinMaxed[i], value4ListMinMaxed[i]]))
# #Let's skip that minmax normalization part
# combinedValuesV3MinMaxed = []
# for i in range(len(dfSC1)):
# maxTemp = max([value2List[i], value3List[i], value4List[i]])
# if(maxTemp > 1.0):
# combinedValuesV3MinMaxed.append(1.0)
# else:
# combinedValuesV3MinMaxed.append(maxTemp)
# combinedValuesV4MinMaxed = []
# for i in range(len(dfSC1)):
# maxTemp = max([temp1List[i], temp2List[i], temp3List[i]])
# if(maxTemp > 1.0):
# combinedValuesV4MinMaxed.append(1.0)
# else:
# combinedValuesV4MinMaxed.append(maxTemp)
# dfSC1['combinedv1'] = combinedValuesV1MinMaxed.round(4)
# dfSC1['combinedv2'] = combinedValuesV2MinMaxed.round(4)
# dfSC1['combinedv3'] = list(np.around(np.array(combinedValuesV3MinMaxed), 4))
# dfSC1['combinedv4'] = list(np.around(np.array(combinedValuesV4MinMaxed), 4))
# #dfSC1['combinedv3'] = combinedValuesV3MinMaxed.round(4)
# # dfSC1['cv'] = dfSC1['cv'].round(4)
# # dfSC1['pc'] = dfSC1['pc'].round(4)
# # dfSC1['tr'] = dfSC1['tr'].round(4)
# # dfSC1['freq'] = dfSC1['freq'].round(1)
# # dfSC1['trace'] = dfSC1['trace'].round(4)
# # dfSC1['clusters'] = dfSC1['clusters'].round(4)
# # dfSC1['clusnumber'] = dfSC1['clusnumber'].round(1)
# # dfSC1['sc1'] = dfSC1['sc1'].round(4)
# # dfSC1['sc2'] = dfSC1['sc2'].round(4)
# # dfSC1['sc3'] = dfSC1['sc3'].round(4)
# dfSC1.to_csv(prot+"_jet.res", header=True, index=None, sep='\t', mode='w')
#dfSC1.to_csv(prot+"_jet.res", header=True, index=None, mode='w')
#sys.exit(-1)
else: else:
......
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