def __init__(self, grofile, pfafile, label, folder):
self.PER_ATOM_FORCE = {}
self.PER_RESIDUE_FORCE = {}
self._folder = folder
self._out_file = self._folder + "/" + "PRF-" + label + ".prf"
self._out_forces = ""
'''
create gro object
create pfa object
'''
self.gro_object = gro_reader.Gro_Reader(grofile)
self.pfa_object = pfa.PFA(pfafile, label)
'''
making a copy of PER_ATOM_FORCE
'''
self.PER_ATOM_FORCE = self.pfa_object.PER_ATOM_FORCE
'''
map forces to each residue
'''
self._per_resid_force()
'''
dump forces to a file
'''
fda_utils.write_file(self._out_file, self._out_forces)
def plot_fun(a, b, FOL, label):
global PNGFOL
fda_gen_util.check_folder(PNGFOL)
xmin = 0
xmax = 200
xgap = 5
fig = plt.figure()
fig.set_size_inches(12, 4)
#N=len(a);
#ind=np.arange(N);
width = 0.5
plt.plot(a, b, color="#02FF00")
#plt.show()
ylab = r'$\Delta\hspace{0.5}\mathrm{Avg.}\hspace{0.5}\mathrm{Force}\hspace{0.5}\mathrm{(pN)}$'
plt.xlabel("Residue id")
plt.ylabel(ylab)
plt.xticks(np.arange(xmin, xmax + xgap, xgap))
plt.xlim(xmin, xmax)
sfname = "%s/%s.png" % (FOL, label)
plt.savefig(sfname, dpi=300)
print("%s saved" % (sfname))
def net_force_calc(ave_force_ref, ave_force_pert, std_ref, std_pert, label,
diff_res):
global FASTA
N = len(ave_force_ref)
net_force_list = []
fac = 1.0 / math.sqrt(N)
wout_data = "%4s %12s %12s %12s %12s\n" % ("ResI", "Net Force", "DELTA F",
"E Ref", "E Pert")
for i in range(N):
error_ref = std_ref[i] * fac
error_pert = 1 #std_pert[i]*fac;
#e_fac=1.0/error_ref
e_fac = 1
i_resid = diff_res[i]
#C=ave_force_pert[i];
D = ave_force_ref[i]
i_diff = 1 #(ave_force_pert[i]-ave_force_ref[i]);
#print i_resid,ave_force_pert[i], ave_force_ref[i]
#wout_data+="%12d %12.2f %12.2f\n"%(diff_res[i],diff_data[i],diff_err[i])
i_net_force = D * e_fac
wout_data += "%4d %12.2f %12.2f %12.2f %12.2f\n" % (
i_resid, i_net_force, i_diff, error_ref, error_pert)
#wout_data+="%4d %12.2f %12.2f %12.2f %12.2f %12.2f\n"%(i_resid,ave_force_ref,ave_force_pert,i_diff,error_ref,error_pert);
if (0 >= i_net_force) or (i_net_force >= 0):
d = [diff_res[i], D]
else:
#d=[FASTA[i],diff_res[i],0]
d = [diff_res[i], 0]
#d=[diff_res[i],i_net_force]
net_force_list.append(d)
osd = fda_gen_util.prep_2d_list_for_writing(net_force_list)
fname = "%s/Net_force-%s.dat" % (os.getcwd(), label)
fda_gen_util.write_file(fname, osd)
return net_force_list
def calc_diff_mode_forces(res_mode_forces_1, res_mode_forces_2, label):
FOL = "/home/mugdha/Projects/TFAM/analysis/fda/png"
print("abra ka dabra")
diff_force_list = []
file_force_list = []
N_1 = len(res_mode_forces_1)
N_2 = len(res_mode_forces_2)
if (N_1 != N_2):
print(
"Residue numbers of PFA1 and PFA2 files are not the ssame. Please check."
)
exit()
for i in range(N_1):
force_diff = res_mode_forces_1[i][1] - res_mode_forces_2[i][1]
diff_force_list.append([res_mode_forces_1[i][0], force_diff])
d = [
i + 1, res_mode_forces_1[i][1], res_mode_forces_2[i][1], force_diff
]
file_force_list.append(d)
osd = fda_gen_util.prep_4d_list_for_writing(file_force_list)
fname = "%s/%s-diff_force_list.dat" % (FOL, label)
fda_gen_util.write_file(fname, osd)
return diff_force_list
def mode_per_resid(force_list, label):
global DUMP_FILES
res_force_list = []
FORCE_LIST = []
N_FRAMES = len(force_list)
for x in range(1):
frame_num = force_list[x][0]
i_resid_force_list = force_list[x][1]
#resid,forces=extract_resid_forces(i_resid_force_list);
resid, forces = fda_gen_util.extract_2d_list(i_resid_force_list)
N_resid = len(resid)
count = 0
FOL = "/home/mugdha/Projects/TFAM/analysis/fda/dat"
for i in range(N_resid):
cur_resid = resid[i]
res_force = []
time_data = []
for j in range(N_FRAMES):
i_data = force_list[j][1][i]
#print force_list
i_resid = int(i_data[0])
i_force = float(i_data[1])
if (i_resid == cur_resid):
res_force.append(i_force)
d = [j, i_force]
time_data.append(d)
#contains time series data per residue can be dumped
#write a dump function for this
#print time_data
'''''
if(DUMP_FILES==True):
osd=fda_gen_util.prep_2d_list_for_writing(time_data)
fname="%s/%s-TvF-%d-TFAM.dat"%(FOL,label,cur_resid)
fda_gen_util.write_file(fname,osd)
new_lab="%s-%s-TimeVsForce"%(label,cur_resid)
a,b=fda_gen_util.extract_2d_list(time_data)
plot_util.plot_fun(a,b,FOL,new_lab)
''' ''
#modforce=stats.mode(res_force)
#d=[cur_resid,modforce[0][0]]
modforce = np.average(res_force)
d = [cur_resid, modforce]
FORCE_LIST.append(d)
return FORCE_LIST
def avg_force_per_frame(force_list):
FORCE_LIST = []
N_FRAMES = len(force_list)
for i in range(N_FRAMES):
frame_num = force_list[i][0]
i_resid_force_list = force_list[i][1]
#resid,forces=extract_resid_forces(i_resid_force_list);
resid, forces = fda_gen_util.extract_2d_list(i_resid_force_list)
ave_force = np.average(forces)
d = [frame_num, ave_force]
print(ave_force[0])
exit()
FORCE_LIST.append(d)
return FORCE_LIST
def _proc_line(self, line):
resid = int(line[0:5].strip())
x = line[20:28].strip()
y = line[28:36].strip()
z = line[36:44].strip()
coord = [x, y, z]
atom_name = line[10:15].strip()
atom_id = int(line[15:20].strip())
res_name = line[5:10].strip()
res_name = utils.aa_map(res_name)
self.ATOM_NAME.append(atom_name)
self.ATOM_ID.append(atom_id)
self.X_Y_Z.append(coord)
self.RES_NAME[resid] = res_name
self._mapping(resid, atom_id)
def _process_meta_file(self):
fda_gen_util.check_file(self._metafile);
meta_open=open(self._metafile,'r');
for i in meta_open:
grofile=self._folder+"/"+i.split()[0]; #indi pfa
pfa_file=self._folder+"/"+i.split()[1]; #indi gro
f_label=i.split()[2]; #indi file label
fda_gen_util.check_file(grofile);
fda_gen_util.check_file(pfa_file);
punc_stress_object=calc_punc_stress.Punctual_Stress(grofile,pfa_file,f_label,self._folder)
def average_force_per_forcefile(a, b, c):
N = len(a)
ave_force_list = []
std_force_list = []
for i in range(N):
all_traj_force = [a[i][1], b[i][1], c[i][1]]
#total_ave_force=all_traj_force/3
#print all_traj_force
total_ave_force = np.average(all_traj_force)
error = np.std(all_traj_force)
#print error
#print total_ave_force#, all_traj_force
d = [a[i][0], error]
s = [a[i][0], total_ave_force]
std_force_list.append(d)
ave_force_list.append(s)
osd = fda_gen_util.prep_2d_list_for_writing(ave_force_list)
#print ave_force_list
#fname="%s/protein-dna-wt_forces.dat"%(os.getcwd());
#fda_gen_util.write_file(fname,osd)
return std_force_list, ave_force_list
def bar_plots(BAR_PLOT_DATA):
#Fol="/home/mugdha/Projects/TFAM/analysis/fda/png/barplots";
global PNGFOL, BPFOL
bar_color = "#0000FF"
fda_gen_util.check_folder(PNGFOL)
#This is a redundant check since BPFOL is nested in PNGFOL
fda_gen_util.check_folder(BPFOL)
fig = plt.figure()
#fig.set_size_inches(12,4)
bar_width = 0.50
opacity = 0.9
labels = []
lab_pos = []
prop = fm.FontProperties(
fname='/usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf')
#prop=fm.FontProperties(fname='/home/mugdha/Downloads/arial.ttf')
N = len(BAR_PLOT_DATA)
ymin = -4000
ymax = 3000
ygap = 1000
title_fs = 12
y_fs = 10
ax1 = plt.subplot(321)
ax_c_data = BAR_PLOT_DATA[0]
ax_label = ax_c_data[0]
ax_plot_data = ax_c_data[1]
N = len(ax_plot_data)
pos_array = []
labels = []
index = np.arange(0, len(ax_plot_data))
for i in range(N):
i_data = ax_plot_data[i]
pos = index[i] + (bar_width * i)
labels.append(i_data[0])
pos_array.append(pos)
plt.bar(pos,
i_data[1],
bar_width,
alpha=opacity,
color=bar_color,
label=i_data[0],
yerr=i_data[2],
ecolor="#000000",
align='center')
plt.xticks(pos_array, labels, fontproperties=prop, fontsize=0)
plt.yticks(np.arange(ymin, ymax + ygap, ygap),
fontproperties=prop,
fontsize=y_fs)
plt.title(ax_label, fontproperties=prop, fontsize=title_fs)
ax2 = plt.subplot(322)
ax_c_data = BAR_PLOT_DATA[1]
ax_label = ax_c_data[0]
ax_plot_data = ax_c_data[1]
N = len(ax_plot_data)
pos_array = []
labels = []
index = np.arange(0, len(ax_plot_data))
for i in range(N):
i_data = ax_plot_data[i]
pos = index[i] + (bar_width * i)
labels.append(i_data[0])
pos_array.append(pos)
plt.bar(pos,
i_data[1],
bar_width,
alpha=opacity,
color=bar_color,
label=i_data[0],
yerr=i_data[2],
ecolor="#000000",
align='center')
plt.xticks(pos_array, labels, fontproperties=prop, fontsize=0)
plt.yticks(np.arange(ymin, ymax + ygap, ygap), fontsize=0)
plt.title(ax_label, fontproperties=prop, fontsize=title_fs)
ax3 = plt.subplot(323)
ax_c_data = BAR_PLOT_DATA[2]
ax_label = ax_c_data[0]
ax_plot_data = ax_c_data[1]
N = len(ax_plot_data)
pos_array = []
labels = []
index = np.arange(0, len(ax_plot_data))
for i in range(N):
i_data = ax_plot_data[i]
pos = index[i] + (bar_width * i)
labels.append(i_data[0])
pos_array.append(pos)
plt.bar(pos,
i_data[1],
bar_width,
alpha=opacity,
color=bar_color,
label=i_data[0],
yerr=i_data[2],
ecolor="#000000",
align='center')
plt.xticks(pos_array, labels, fontproperties=prop, fontsize=0)
plt.yticks(np.arange(ymin, ymax + ygap, ygap),
fontproperties=prop,
fontsize=y_fs)
plt.title(ax_label, fontproperties=prop, fontsize=title_fs)
ax4 = plt.subplot(324)
ax_c_data = BAR_PLOT_DATA[3]
ax_label = ax_c_data[0]
ax_plot_data = ax_c_data[1]
N = len(ax_plot_data)
pos_array = []
labels = []
index = np.arange(0, len(ax_plot_data))
for i in range(N):
i_data = ax_plot_data[i]
pos = index[i] + (bar_width * i)
labels.append(i_data[0])
pos_array.append(pos)
plt.bar(pos,
i_data[1],
bar_width,
alpha=opacity,
color=bar_color,
label=i_data[0],
yerr=i_data[2],
ecolor="#000000",
align='center')
plt.xticks(pos_array, labels, fontproperties=prop, fontsize=0)
plt.yticks(np.arange(ymin, ymax + ygap, ygap), fontsize=0)
plt.title(ax_label, fontproperties=prop, fontsize=title_fs)
ax5 = plt.subplot(325)
ax_c_data = BAR_PLOT_DATA[4]
ax_label = ax_c_data[0]
ax_plot_data = ax_c_data[1]
N = len(ax_plot_data)
pos_array = []
labels = []
index = np.arange(0, len(ax_plot_data))
for i in range(N):
i_data = ax_plot_data[i]
pos = index[i] + (bar_width * i)
labels.append(i_data[0])
pos_array.append(pos)
plt.bar(pos,
i_data[1],
bar_width,
alpha=opacity,
color=bar_color,
label=i_data[0],
yerr=i_data[2],
ecolor="#000000",
align='center')
plt.title(ax_label, fontproperties=prop, fontsize=title_fs)
plt.xticks(pos_array, labels, fontproperties=prop, fontsize=10)
plt.yticks(np.arange(ymin, ymax + ygap, ygap),
fontproperties=prop,
fontsize=y_fs)
plt.ylim(ymin, ymax)
ax6 = plt.subplot(326)
ax_c_data = BAR_PLOT_DATA[5]
ax_label = ax_c_data[0]
ax_plot_data = ax_c_data[1]
N = len(ax_plot_data)
pos_array = []
labels = []
index = np.arange(0, len(ax_plot_data))
for i in range(N):
i_data = ax_plot_data[i]
pos = index[i] + (bar_width * i)
labels.append(i_data[0])
pos_array.append(pos)
plt.bar(pos,
i_data[1],
bar_width,
alpha=opacity,
color=bar_color,
label=i_data[0],
yerr=i_data[2],
ecolor="#000000",
align='center')
plt.xticks(pos_array, labels, fontproperties=prop, fontsize=10)
plt.yticks(np.arange(ymin, ymax + ygap, ygap), fontsize=0)
#plt.setp(ax6.get_yticklabels(),visible=False);
plt.title(ax_label, fontproperties=prop, fontsize=title_fs)
#plt.legend(prop=mplot.font_manager.FontProperties(size=10),frameon=False,ncol=2,mode="expand")
plt.tight_layout()
sfname = "%s/MutationL6-barPlot.png" % (BPFOL)
plt.savefig(sfname, dpi=300)
#plt.show()
''''