#snag the RSA values RSA = natural_data[0] #get a list of char amino acid codes AA = resdict.values() #print "Dimensions of array {}".format(natural_data.ndim) n,m = natural_data.shape n_d, m_d = designed_data.shape #print "Natural Protein Length", m #print "Designed Protein Length", m_d designed_protein_lengths.append(m_d) #Open .dat files that you will write the results to. natural_fpW = open("align_natural_data_array_" + pdb_id + "_" + chain_id + ".dat","w") natural_fpW.write(af.dump_csv_line2(rows)) designed_fpW = open("align_data_array_" + pdb_id + "_" + chain_id + "_" + method + ".dat","w") designed_fpW.write(af.dump_csv_line2(rows)) counter = 0 natural_aaSum = 0 designed_aaSum = 0 aaCutOff = 0 #This is cut-off. If a site has less than this percent of amino acids left after taking out the gaps do not use that site in the analysis. for i in range(0,m): #This blocks calculates the site frequency data natural_aaCount = [] designed_aaCount = [] #covert back to list so we can use the "count" method try: natural_aaList = list(natural_data[1:,i]) designed_aaList = list(designed_data[1:,i]) except IndexError:
natural_sample2_data = np.array(natural_sample2_data, dtype=object) #snag the RSA values RSA = natural_data[0] #get a list of char amino acid codes AA = resdict.values() #print "Dimensions of array {}".format(natural_data.ndim) n, m = natural_data.shape n_sample1, m_sample = natural_sample1_data.shape #Open .dat files that you will write the results to. natural_sample1_fpW = open( "align_natural_sample1_data_array_" + pdb_id + "_" + chain_id + ".dat", "w") natural_sample1_fpW.write(af.dump_csv_line2(rows)) natural_sample2_fpW = open( "align_natural_sample2_data_array_" + pdb_id + "_" + chain_id + ".dat", "w") natural_sample2_fpW.write(af.dump_csv_line2(rows)) counter = 0 natural_sample1_aaSum = 0 natural_sample2_aaSum = 0 for i in range(0, m): #Calculates the site frequency data natural_aaCount = [] designed_aaCount = [] natural_sample1_aaCount = [] natural_sample2_aaCount = [] #covert back to list so we can use the "count" method
if(debug): print len(natural_sample2_data[i]) natural_sample2_data = np.array(natural_sample2_data, dtype = object) #snag the RSA values RSA = natural_data[0] #get a list of char amino acid codes AA = resdict.values() #print "Dimensions of array {}".format(natural_data.ndim) n,m = natural_data.shape n_sample1, m_sample = natural_sample1_data.shape #Open .dat files that you will write the results to. natural_sample1_fpW = open("align_natural_sample1_data_array_ordered_" + pdb_id + "_" + chain_id + ".dat","w") natural_sample1_fpW.write(af.dump_csv_line2(rows)) natural_sample2_fpW = open("align_natural_sample2_data_array_ordered_" + pdb_id + "_" + chain_id + ".dat","w") natural_sample2_fpW.write(af.dump_csv_line2(rows)) counter = 0 natural_aaSum = 0 designed_aaSum = 0 natural_sample1_aaSum = 0 natural_sample2_aaSum = 0 for i in range(0,m): natural_aaCount = [] designed_aaCount = [] natural_sample1_aaCount = [] natural_sample2_aaCount = [] #covert back to list so we can use the "count" method
#if(debug): # print len(designed_data[i]) designed_data = np.array(designed_data, dtype = object) #snag the RSA values RSA = natural_data[0] #get a list of char amino acid codes AA = resdict.values() #print "Dimensions of array {}".format(natural_data.ndim) n,m = natural_data.shape #Open .dat files that you will write the results to. natural_fpW = open("align_natural_data_array_ordered_" + pdb_id + "_" + chain_id + ".dat","w") natural_fpW.write(af.dump_csv_line2(rows)) designed_fpW = open("align_data_array_ordered_" + pdb_id + "_" + chain_id + "_" + "soft" + ".dat","w") designed_fpW.write(af.dump_csv_line2(rows)) counter = 0 natural_aaSum = 0 designed_aaSum = 0 aaCutOff = 0 #This is cut-off. If a site has less than this percent of amino acids left after taking out the gaps do not use that site in the analysis. for i in range(0,m): #This blocks calculates the site frequency data natural_aaCount = [] designed_aaCount = [] #covert back to list so we can use the "count" method try: natural_aaList = list(natural_data[1:,i]) designed_aaList = list(designed_data[1:,i]) except IndexError:
RSA = natural_data[0] #get a list of char amino acid codes AA = resdict.values() #print "Dimensions of array {}".format(natural_data.ndim) n, m = natural_data.shape n_d, m_d = designed_data.shape #print "Natural Protein Length", m #print "Designed Protein Length", m_d designed_protein_lengths.append(m_d) #Open .dat files that you will write the results to. natural_fpW = open( "align_natural_data_array_" + pdb_id + "_" + chain_id + ".dat", "w") natural_fpW.write(af.dump_csv_line2(rows)) designed_fpW = open( "align_data_array_" + pdb_id + "_" + chain_id + "_" + method + ".dat", "w") designed_fpW.write(af.dump_csv_line2(rows)) counter = 0 natural_aaSum = 0 designed_aaSum = 0 aaCutOff = 0 #This is cut-off. If a site has less than this percent of amino acids left after taking out the gaps do not use that site in the analysis. for i in range(0, m): #This blocks calculates the site frequency data natural_aaCount = [] designed_aaCount = [] #covert back to list so we can use the "count" method try: natural_aaList = list(natural_data[1:, i])