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FitTransform.py
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FitTransform.py
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import pandas as pd
import numpy as np
from math import cos,sin,tan,asin,acos,radians,sqrt,degrees,atan,copysign,atan2
import math
import util.RotationMethods as rm
import sys
import argparse
#--------------------------General Methods-----------------------------
def prepData_Str(data_frame, rmsd_filter = 101):
"""Convert to right data types csv file read from pandas. Filters rmsd, removes unused parameters."""
df = data_frame.copy()
df = df.loc[df['name'] != 'name']
for col in df.columns:
t="float"
if "length" in col:
t = "integer"
elif 'name' in col:
continue
df[col] = pd.to_numeric(df[col],downcast=t)
#remove poor fitting rmsd
df.insert(1, 'max_rmsd', df[df.filter(like='rmsd').columns].max(axis=1))
df = df.loc[df['max_rmsd']<rmsd_filter]
exParams = ['omega1','d', 'r1'] #d removes rmsd as well
for x in exParams:
df=df.drop(df.filter(like=x,axis=1).columns,axis=1)
df = df.dropna()
return df
def dist1(p1,p2):
return np.linalg.norm(p2-p1)
def generate_distLabel(num_helices=4):
numEndpoints = num_helices*2
distLabels = []
for x in range(1,numEndpoints+1):
for y in range(x+1,numEndpoints+1):
distLabels.append(f'd_{x}_{y}')
return distLabels
def saveAsNumpy(df,name,direc=''):
fullNumpy = df.to_numpy()
names = fullNumpy[:,-1]
feats = fullNumpy[:,:-1]
fnames = df.columns[:-1]
print(feats.shape)
np.savez_compressed(f'{direc}{name}.npz',X_train=feats, y_train=names , featNames=fnames)
def saveAs_CSV(df,name,direc=''):
df.to_csv(f'{direc}{name}')
def drop_extra_parameters(df,exParams = ['phi1']):
dfOut = df.copy()
for x in exParams:
dfOut=dfOut.drop(dfOut.filter(like=x,axis=1).columns,axis=1)
return dfOut
def df_to_dictList(dfIn,numHelices=4):
labels = dfIn.columns
npVal = dfIn.to_numpy()
#remove name
labels = labels[1:]
npVal = npVal[:,1:]
tot = int(len(labels)/4)
#remove helix identifier (_X)
keys = []
for x in range(tot):
keys.append(labels[x][:-2])
npVal = npVal.reshape(npVal.shape[0],numHelices,-1)
dListList = []
for i in range(npVal.shape[0]):
dList = []
for j in range(npVal[i].shape[0]):
dList.append(dict(zip(keys,npVal[i][j])))
dListList.append(dList)
return dListList
#------------------Conversion of HelixParameters to other Representations--------------------
def EndPoint(dfIn,num_helices=4):
"""Uses panda converts HelicalParameters to Endpoints."""
df = dfIn.copy()
zUnit = np.array([0,0,-1] ,dtype=np.float64)
labels = ['x1','y1','z1','x2','y2','z2','x3','y3','z3',
'x4','y4','z4','x5','y5','z5','x6','y6','z6',
'x7','y7','z7','x8','y8','z8']
dicList = []
for i in range(df.shape[0]):
pointList = []
eulerList = []
for j in range(num_helices):
xNum = df.columns.get_loc(f'translate_x_{j+1}')
yNum = df.columns.get_loc(f'translate_y_{j+1}')
zNum = df.columns.get_loc(f'translate_z_{j+1}')
pointList.append(np.array([df.iloc[i,xNum], df.iloc[i,yNum], df.iloc[i,zNum]],dtype=np.float64))
phiNum = df.columns.get_loc(f'rotate_phi_{j+1}')
thetaNum = df.columns.get_loc(f'rotate_theta_{j+1}')
psiNum = df.columns.get_loc(f'rotate_psi_{j+1}')
eulerList.append(np.array([df.iloc[i,phiNum], df.iloc[i,thetaNum], df.iloc[i,psiNum]],dtype=np.float64))
endPointList = []
for j in range(len(pointList)):
R = rm.euler_to_R(eulerList[j][0],eulerList[j][1],eulerList[j][2])
vec = rm.normalize(np.matmul(R,zUnit))
lengthNum = df.columns.get_loc(f'length_{j+1}')
length = df.iloc[i,lengthNum]
risePerRes = 1.51
multLen = int(length/2)
revMultLen = multLen
if length % 2 != 0:
multLen += 1
multLen = multLen*risePerRes
revMultLen = revMultLen*risePerRes
p1 = pointList[j]-revMultLen*vec
p2 = pointList[j]+multLen*vec
endPointList.extend(p1)
endPointList.extend(p2)
dicList.append(dict(zip(labels,endPointList)))
for j in range(num_helices):
dicList[-1][f'phi1_{j+1}'] = df.iloc[i,df.columns.get_loc(f'phi1_{j+1}')]
dicList[-1]['name'] = df.iloc[i,df.columns.get_loc('name')]
dfOut = pd.DataFrame(dicList, columns=dicList[0].keys())
return dfOut
def contactDist_EndPoint(dfIn,num_helices=4):
"""Converts Parameters form HelixParameter to endpoints then to endpoint distance map."""
#Used for Representing Protein in GAN
# labels = ['d_1_2', 'd_1_3', 'd_1_4', 'd_1_5', 'd_1_6', 'd_1_7', 'd_1_8', 'd_2_3', 'd_2_4',
# 'd_2_5', 'd_2_6', 'd_2_7', 'd_2_8', 'd_3_4', 'd_3_5', 'd_3_6', 'd_3_7', 'd_3_8',
# 'd_4_5','d_4_6', 'd_4_7', 'd_4_8', 'd_5_6', 'd_5_7', 'd_5_8', 'd_6_7', 'd_6_8',
# 'd_7_8']
labels = generate_distLabel(num_helices=num_helices)
iu1 = np.triu_indices(num_helices*2, 1) #indices for upper right triangle , no diagonal
zUnit = np.array([0,0,-1] ,dtype=np.float64)
df = dfIn.copy()
dicList = []
for i in range(df.shape[0]):
pointList = []
eulerList = []
for j in range(num_helices):
xNum = df.columns.get_loc(f'translate_x_{j+1}')
yNum = df.columns.get_loc(f'translate_y_{j+1}')
zNum = df.columns.get_loc(f'translate_z_{j+1}')
pointList.append(np.array([df.iloc[i,xNum], df.iloc[i,yNum], df.iloc[i,zNum]],dtype=np.float64))
phiNum = df.columns.get_loc(f'rotate_phi_{j+1}')
thetaNum = df.columns.get_loc(f'rotate_theta_{j+1}')
psiNum = df.columns.get_loc(f'rotate_psi_{j+1}')
eulerList.append(np.array([df.iloc[i,phiNum], df.iloc[i,thetaNum], df.iloc[i,psiNum]],dtype=np.float64))
endPointList = []
for j in range(len(pointList)):
R = rm.euler_to_R(eulerList[j][0],eulerList[j][1],eulerList[j][2])
vec = rm.normalize(np.matmul(R,zUnit))
lengthNum = df.columns.get_loc(f'length_{j+1}')
length = df.iloc[i,lengthNum]
risePerRes = 1.51
revVec = -vec
multLen = int(length/2)
revMultLen = multLen
if length % 2 != 0:
multLen += 1
multLen = multLen*risePerRes
revMultLen = revMultLen*risePerRes
p1 = pointList[j]-revMultLen*vec
p2 = pointList[j]+multLen*vec
endPointList.append(p1)
endPointList.append(p2)
distMap = np.zeros((len(endPointList),len(endPointList)),dtype=np.float64)
for x in range(len(endPointList)):
for y in range(len(endPointList)):
distMap[x][y] = dist1(endPointList[x],endPointList[y])
distC = distMap[iu1]
dicList.append(dict(zip(labels,distC)))
for j in range(num_helices):
dicList[-1][f'phi1_{j+1}'] = df.iloc[i,df.columns.get_loc(f'phi1_{j+1}')]
dicList[-1]['name'] = df.iloc[i,df.columns.get_loc('name')]
dfOut = pd.DataFrame(dicList, columns=dicList[0].keys())
return dfOut
# In[3]:
def contact_Dist_Dihedral(dfIn,num_helices=4):
"""Converts HelixParameters to contact_dist(helix midpoint) and dihedrals angles between each helix vector."""
#Used for clustering
labels = ['d_1_2', 'd_1_3', 'd_1_4', 'd_2_3', 'd_2_4', 'd_3_4',
'a_1_2', 'a_1_3', 'a_1_4', 'a_2_3', 'a_2_4', 'a_3_4']
iu1 = np.triu_indices(num_helices, 1) #indices for upper right triangle , no diagonal
df = dfIn.copy()
dicList = []
for i in range(df.shape[0]):
pointList = []
eulerList = []
for j in range(num_helices):
xNum = df.columns.get_loc(f'translate_x_{j+1}')
yNum = df.columns.get_loc(f'translate_y_{j+1}')
zNum = df.columns.get_loc(f'translate_z_{j+1}')
pointList.append(np.array([df.iloc[i,xNum], df.iloc[i,yNum], df.iloc[i,zNum]],dtype=np.float64))
phiNum = df.columns.get_loc(f'rotate_phi_{j+1}')
thetaNum = df.columns.get_loc(f'rotate_theta_{j+1}')
psiNum = df.columns.get_loc(f'rotate_psi_{j+1}')
eulerList.append(np.array([df.iloc[i,phiNum], df.iloc[i,thetaNum], df.iloc[i,psiNum]],dtype=np.float64))
distMap = np.zeros((len(pointList),len(pointList)),dtype=np.float64)
diheMap = np.zeros((len(eulerList),len(eulerList)),dtype=np.float64)
for x in range(len(pointList)):
for y in range(len(pointList)):
distMap[x][y] = dist1(pointList[x],pointList[y])
diheMap[x][y] = rm.dihedral_from_euler(pointList[x],pointList[y],eulerList[x],eulerList[y])
dist = distMap[iu1]
dihe = diheMap[iu1]
dist_dihe=np.hstack((dist,dihe))
dicList.append(dict(zip(labels,dist_dihe)))
for j in range(num_helices):
dicList[-1][f'phi1_{j+1}'] = df.iloc[i,df.columns.get_loc(f'phi1_{j+1}')]
dicList[-1][f'length_{j+1}'] = df.iloc[i,df.columns.get_loc(f'length_{j+1}')]
dicList[-1]['name'] = df.iloc[i,df.columns.get_loc('name')]
dfOut = pd.DataFrame(dicList, columns=dicList[0].keys())
#d
return dfOut
def convert_to_endpoints_distmap(inFile,outFile,rmsd_max = 10):
"""Converst csv save of HelixParameters to endpoint distance map for GAN"""
dfRead = pd.read_csv(inFile)
df1 = prepData_Str(dfRead,rmsd_filter=rmsd_max)
df2 = contactDist_EndPoint(df1)
saveAsNumpy(df2,outFile)
def convert_to_endpoints(inFile,outFile,rmsd_max = 10):
"""Converts csv save of HelixParameters to endpoint distance map for GAN"""
dfRead = pd.read_csv(inFile)
df1 = prepData_Str(dfRead,rmsd_filter=rmsd_max)
df2 = EndPoint(df1)
saveAsNumpy(df2,outFile)
def convert_to_distdihe(inFile,outFile,rmsd_max = 10):
"""Converts csv save of HelixParameters to endpoint distance map for GAN"""
dfRead = pd.read_csv(inFile)
df1 = prepData_Str(dfRead,rmsd_filter=rmsd_max)
df2 = contact_Dist_Dihedral(df1)
saveAsNumpy(df2,outFile)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Converts Straight Helix Parameters into other formats. -d for GAN training")
parser.add_argument("infile", help="CSV of Helix Parameters")
parser.add_argument("outfile", help="Output File Name")
parser.add_argument("-e", "--endpoints", help="Output Data as Endpoints / Phi", action="store_true")
parser.add_argument("-d", "--distancemap", help="Output Data as Distance Map / Phi", action="store_true")
parser.add_argument("-c", "--distdihe_cluster", help="Output Data as Distance/Dihedrals", action="store_true")
parser.add_argument("-r", "--rmsd_cut", help="Cut off for RMSD of Fitting", default=10.0, type=float)
args = parser.parse_args()
if args.endpoints:
convert_to_endpoints(args.infile,args.outfile, rmsd_max=args.rmsd_cut)
if args.distancemap:
convert_to_endpoints_distmap(args.infile,args.outfile, rmsd_max=args.rmsd_cut)
elif args.distdihe_cluster:
convert_to_distdihe(args.infile,args.outfile, rmsd_max=args.rmsd_cut)
else:
convert_to_endpoints_distmap(args.infile,args.outfile, rmsd_max=args.rmsd_cut)