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analysis_parallel.py
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analysis_parallel.py
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from supporting_functions import *
from sklearn.cluster import AgglomerativeClustering
import run_locusts_on_analysis
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
def cart2pol(x, y):
rho = np.sqrt(x ** 2 + y ** 2)
phi = np.arctan2(y, x)
return (rho, phi)
def pol2cart(rho, phi):
x = rho * np.cos(phi)
y = rho * np.sin(phi)
return (x, y)
def con(x, r=[]):
res = []
for i in range(2, len(x), 2):
res.append(r[int(i / 2)] ** 2 - x[i] ** 2 - x[i + 1] ** 2)
return res
def equilibrium_ENM(posMX, relaxdMX, stiffMX, radialc):
con1 = {'type': 'eq', 'fun': con, 'args': [radialc]}
bounds = [[radialc[0], radialc[0]], [0, 0]] + [[None, None]] * (posMX.shape[0] * 2 - 2)
newposMX = scipy.optimize.minimize(energy_ENM, posMX.flatten(),
args=(relaxdMX, stiffMX),
method='trust-constr',
constraints=[con1]).x.reshape((-1, 2))
return newposMX
def energy_ENM(posMX, relaxdMX, stiffMX):
# The argument P is a vector (flattened matrix).
# We convert it to a matrix here.
P = posMX.reshape((-1, 2))
L = relaxdMX # .reshape(())
K = stiffMX # .reshape((-1, 1))
# We compute the distance matrix.
D = scipy.spatial.distance_matrix(P, P)
# The potential energy is the sum of the
# gravitational and elastic potential energies.
return .5 * (K * (D - L) ** 2).sum() # element-wise (Hadamard) multiplication...
def colorbar_index(ncolors, cmap, ntm, cax=None, horiz=False):
if horiz:
cmap = cmap_discretize(cmap, ncolors)
mappable = cm.ScalarMappable(cmap=cmap)
mappable.set_array([])
mappable.set_clim(-0.5, ncolors + 0.5)
colorbar = plt.colorbar(mappable, cax=cax, ticks=[], orientation='horizontal', fraction=0.046, pad=0.04)
colorbar.outline.set_visible(False)
colorbar.ax.get_yaxis().set_ticks([])
for j, lab in [(0, '#TM -5'), (5.5, '#TM ({0})'.format(ntm)), (11, '#TM +5')]:
colorbar.ax.text(j, 5, lab, ha='center', va='center')
else:
cmap = cmap_discretize(cmap, ncolors)
mappable = cm.ScalarMappable(cmap=cmap)
mappable.set_array([])
mappable.set_clim(-0.5, ncolors + 0.5)
colorbar = plt.colorbar(mappable, fraction=0.046, pad=0.04)
colorbar.set_ticks(np.linspace(0, ncolors, ncolors))
colorbar.set_ticklabels(['#TM -5', '', '', '', '', '#TM ({0})'.format(ntm), '', '', '', '', '#TM +5'])
return
def cmap_discretize(cmap, N):
"""Return a discrete colormap from the continuous colormap cmap.
cmap: colormap instance, eg. cm.jet.
N: number of colors.
Example
x = resize(arange(100), (5,100))
djet = cmap_discretize(cm.jet, 5)
imshow(x, cmap=djet)
"""
if type(cmap) == str:
cmap = plt.get_cmap(cmap)
colors_i = np.concatenate((np.linspace(0, 1., N), (0., 0., 0., 0.)))
colors_rgba = cmap(colors_i)
indices = np.linspace(0, 1., N + 1)
cdict = {}
for ki, key in enumerate(('red', 'green', 'blue')):
cdict[key] = [(indices[i], colors_rgba[i - 1, ki], colors_rgba[i, ki]) for i in range(N + 1)]
# Return colormap object.
return mcolors.LinearSegmentedColormap(cmap.name + "_%d" % N, cdict, 1024)
def neighborlists(options, sorted_table_fn, an_locations,
no_print=False):
### WARNING: ASSUMES summary_table IS SORTED!
SEQTHR = float(options['PARAMETERS'][('seqid_thr', 'sequence_identity_threshold')])
STRTHR = float(options['PARAMETERS'][('tmscore_thr', 'tmscore_threshold')])
pdbi_chs = [] # PDBI_CHs for which a neighbor list has been compiled
pdbi_chs2 = set()
numtotneighs = {}
wref = 'XXX'
open_files = {}
with open(sorted_table_fn) as stf:
for line in stf:
pdbi1, ch1, pdbi2, ch2, alnlen, seq_seqid, str_seqid, rmsd, tmscore = line.split()
pdbi_ch1 = pdbi1 + "_" + ch1
pdbi_ch2 = pdbi2 + "_" + ch2
pdbi_chs2.add(pdbi_ch2)
if not no_print and pdbi1 != wref:
wref = pdbi1
for pc in open_files:
for f in open_files[pc]:
f.close()
open_files = {}
if pdbi_ch1 not in pdbi_chs:
pdbi_chs.append(pdbi_ch1)
numtotneighs[pdbi_ch1] = 0
if not no_print:
seqneigh_fn = an_locations['FSYSPATH']['seqneighs'] + 'seqneigh_' + pdbi_ch1 + '.txt'
strneigh_fn = an_locations['FSYSPATH']['strneighs'] + 'strneigh_' + pdbi_ch1 + '.txt'
totneigh_fn = an_locations['FSYSPATH']['totneighs'] + 'totneigh_' + pdbi_ch1 + '.txt'
segment_fn = an_locations['FSYSPATH']['totneighs'] + 'segment_' + pdbi_ch1 + '.txt'
seqneigh_f = open(seqneigh_fn, 'w')
strneigh_f = open(strneigh_fn, 'w')
totneigh_f = open(totneigh_fn, 'w')
segment_f = open(segment_fn, 'w')
open_files[pdbi_ch1] = [seqneigh_f, strneigh_f, totneigh_f, segment_f]
if float(seq_seqid) > SEQTHR or float(tmscore) > STRTHR:
numtotneighs[pdbi_ch1] += 1
if not no_print:
open_files[pdbi_ch1][2].write("{0}\t{1}\t{2}\n".format(pdbi_ch2, seq_seqid, tmscore))
if no_print:
continue
if float(seq_seqid) > SEQTHR:
open_files[pdbi_ch1][0].write("{0}\t{1}\n".format(pdbi_ch2, seq_seqid))
if float(tmscore) > STRTHR:
open_files[pdbi_ch1][1].write("{0}\t{1}\n".format(pdbi_ch2, tmscore))
open_files[pdbi_ch1][3].write("{0}\t{1}\t{2}\n".format(pdbi_ch2, seq_seqid, tmscore))
print("ENTRIES ONLY IN COL2", pdbi_chs2 - set(pdbi_chs))
print("ENTRIES ONLY IN COL1", set(pdbi_chs) - pdbi_chs2)
pdbi_chs = sorted(list(set(pdbi_chs)))
return pdbi_chs, numtotneighs
def cdist(c1, c2):
return ((c1[0] - c2[0]) ** 2 + (c1[1] - c2[1]) ** 2) ** 0.5
def find_disposition(tm, ccoord, pcoord, placed, j):
n = len(placed)
sampled_pcoord = np.zeros((360, 2))
sampled_ccoord = np.zeros((360, 2))
for a in range(sampled_pcoord.shape[0]):
theta = 2 * math.pi * a / sampled_pcoord.shape[0]
sampled_pcoord[a] = (tm[0][j], theta)
sampled_ccoord[a] = (tm[0][j] * math.cos(theta), tm[0][j] * math.sin(theta))
# Sum the potential
V = np.zeros(360)
for a in range(V.shape[0]):
for i in placed:
# print(a, i, ccoord[i], sampled_ccoord[a])
d = cdist(ccoord[i], sampled_ccoord[a])
d0 = tm[i][j]
V[a] += (d - d0) ** 2
am = V.argmin()
return sampled_pcoord[am], sampled_ccoord[am]
def gif_instr_creator(options, locations, neighbors, names):
this_name = 'gif_creator'
tic = time.time()
script_folder = os.getcwd() + '/'
fnull = open(os.devnull, 'w')
pymolpath = options['pymol_path']
for pdbname in sorted(list(names.keys())):
pdb_filename = locations['']
pml_filename = locations['FSYS']['images_pml_whole'] + pdbname + '.pml'
fig_filename = locations['FSYS']['images_figs_whole'] + pdbname + '.png'
pml_file = open(pml_filename, 'w')
pml_file.write(create_whole_png_text(pdb_filename, fig_filename))
pml_file.close()
os.system("cd {0}; {1} -ucq {2}; cd -".format(locations['analysis'], pymolpath, pml_filename))
for struct in sorted(list(neighbors.keys())):
pml_filename = locations['FSYS']['images_pml_chain'] + struct + '.pml'
fig_filename = locations['FSYS']['images_figs_chain'] + struct + '.png'
pdbname = struct[:4]
chain = struct[5]
pml_file = open(pml_filename, 'w')
pml_file.write(create_chain_png_text(pdb_filename, chain, fig_filename))
pml_file.close()
os.system("cd {0}; {1} -ucq {2}; cd -".format(locations['analysis'], pymolpath, pml_filename))
toc = time.time()
print_log(this_name, "Time spent: {0}".format(toc - tic))
def gen_cartesian_randomphi(distV):
# Returns a vector of length len(distV)+1 od 2D polar coords, with the first one being (0,0)
cres = np.zeros((len(distV), 2))
pres = np.zeros((len(distV), 2))
for i in range(len(distV)):
randphi = 2 * np.pi * random.random()
if distV[i] == 0:
randphi = 0
pres[i][:] = (distV[i], randphi)
cres[i][:] = pol2cart(distV[i], randphi)
return cres, pres
def polar(options, locations, entry_list, ntms, only_write=False, do_only=None, do_eps=False, do_pdf=False):
this_name = 'polar' ## CHANGE
print("! Creating polar plot", do_pdf)
print("NTMS", ntms)
# Figure parameters
figsize = (8, 8)
points_per_inch = 72.272 # Conversion factor
clickmap = {} # Dict containing for each entry a list of 3 same-length lists with names, xcoord and ycoord of the pixels
N = len(entry_list)
# Extract TMscore and sequence identity AS METRICS
tm = [] # Includes self as first
tmlabels = [] # Includes self as first
# Record all the TMscores from all entries
for pdbi_ch1 in entry_list:
tm.append([1])
tmlabels.append([pdbi_ch1])
strneigh_fn = locations['FSYSPATH']['strneighs'] + 'strneigh_' + pdbi_ch1 + '.txt'
with open(strneigh_fn) as strneigh_f:
for line in strneigh_f:
pdbi_ch2, tmscore = line.split()
tm[-1].append(float(tmscore))
tmlabels[-1].append(pdbi_ch2)
# Main loop
if do_only:
en = [(b, c) for b, c in enumerate(entry_list) if c in do_only]
print("DO ONLY", do_only)
print("EN", en)
else:
en = list(enumerate(entry_list))
for i1, pdbi_ch1 in en:
# Build the TM matrix
subN = len(tm[i1])
subtm = np.ones((subN, subN)) # indexes relative to pdbi_ch1
print("ITSELF", pdbi_ch1)
print("NEIGHBORS", tmlabels[i1])
print("NUMBER OF NEIGHBORS", subN)
for j1, tm1 in enumerate(tm[i1]): # [(ii+1, x) for ii, x in enumerate(tm[i1])]:
# j1 in [1, len(tm[i1])+1)
# subtm[0, j1] = subtm[j1, 0] = tm1
j1_ind = entry_list.index(tmlabels[i1][j1])
for j2, tmlabel2 in [(h, c) for h, c in enumerate(tmlabels[i1]) if
h > j1]: # nonj2 is an index relative to a neighbor of pdbi_ch1, must be converted
if tmlabel2 in tmlabels[j1_ind]:
j2_ind = tmlabels[j1_ind].index(tmlabel2)
subtm[j1, j2] = subtm[j2, j1] = tm[j1_ind][j2_ind]
subtm = 1. - subtm
# cpos0, ppos0 = gen_cartesian_randomphi(subtm[0]) # pos0.shape == subtm.shape
ccoords, pcoords = -np.ones((subtm.shape[0], 2)), -np.ones((subtm.shape[0], 2))
build_matrix = 10 * np.ones(subtm.shape)
build_matrix[0] = np.copy(subtm[0])
placed = set()
placed0 = set() # set([0])
while len(placed) + len(placed0) < subtm.shape[0]:
# Flatten the matrix and decide an order for putting points
linear_form = []
for nn in range(build_matrix.shape[0]):
for mm in range(build_matrix.shape[1]):
linear_form.append((nn, mm, build_matrix[nn][mm]))
linear_form = sorted(linear_form,
key=lambda x: (x[2], x[0])) # Order: from the closest to the farthest from the center
for tr in linear_form:
if tr[0] == 0 and tr[2] == 0 and tr[1] not in placed0:
placed0.add(tr[1])
pcoords[tr[1]][0] = 0.0
pcoords[tr[1]][1] = 0.0
ccoords[tr[1]][0] = 0.0
ccoords[tr[1]][1] = 0.0
continue
elif tr[1] not in placed and tr[1] not in placed0:
pcoords[tr[1]], ccoords[tr[1]] = find_disposition(build_matrix, ccoords, pcoords, placed, tr[1])
build_matrix[tr[1]] = np.copy(subtm[tr[1]])
placed.add(tr[1])
break
ntm_diff = [] # Difference in number of TM regions
coord_fn = locations['FSYSPATH']['polar_data'] + '{0}_neigh.txt'.format(pdbi_ch1)
pdbi1, ch1 = pdbi_ch1.split('_')
with open(coord_fn, 'w') as coord_f:
coord_f.write('#PDBid\t{1:8}\t{2:8}\t{3}\n'.format(pdbi_ch1, 'rho(=TM)', 'theta', '#MemReg'))
for i in range(ccoords.shape[0]):
pdbi2, ch2 = tmlabels[i1][i].split('_')
coord_f.write('{0}\t{1:8.5f}\t{2:8.5f}\t{3}\n'.format(tmlabels[i1][i], pcoords[i][0], pcoords[i][1],
ntms[pdbi2][ch2]))
ntm_diff.append(0.5 - 0.1 * (ntms[pdbi1][ch1] - ntms[pdbi2][ch2]))
rhos, thetas = pcoords[:, 0], pcoords[:, 1]
# Plot
fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection='polar'))
ax.axes.get_xaxis().set_ticks([])
plt.yticks([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4], ['', '0.9', '', '0.8', '', '0.7', '', '0.6'])
ax.set_axisbelow(True)
im = ax.scatter(thetas, rhos, c=ntm_diff, cmap='coolwarm_r', edgecolors='k')
im.set_clim(0.0, 1.0)
cmap = plt.get_cmap('coolwarm_r')
colorbar_index(ncolors=11, cmap=cmap, ntm=ntms[pdbi1][ch1], horiz=True)
ax.set_rmin(0.0)
ax.set_rmax(0.4)
fig.canvas.draw()
figname = locations['FSYSPATH']['polar_figs'] + 'p_' + pdbi_ch1 + ".png"
plt.savefig(figname, dpi=fig.dpi)
if do_eps:
plt.savefig(figname[:-4] + '.eps', dpi=fig.dpi)
if do_pdf:
plt.savefig(figname[:-4] + '.pdf')
plt.close(fig)
fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection='polar'))
ax.axes.get_xaxis().set_ticks([])
plt.yticks([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4], ['', '0.9', '', '0.8', '', '0.7', '', '0.6'])
ax.plot(thetas, rhos, 'bo')
im = ax.scatter(thetas, rhos, c=ntm_diff, cmap='coolwarm_r', edgecolors='k')
im.set_clim(0.0, 1.0)
cmap = plt.get_cmap('coolwarm_r')
colorbar_index(ncolors=11, cmap=cmap, ntm=ntms[pdbi1][ch1], horiz=True)
ax.set_rmin(0.0)
ax.set_rmax(0.4)
fig.canvas.draw()
pix_xy = ax.transData.transform(np.vstack([thetas, rhos]).T) # Take pixels
pix_x, pix_y = pix_xy.T
width, height = fig.canvas.get_width_height()
pix_y = height - pix_y
clickmap = list(zip(tmlabels[i1], pix_x, pix_y))
clickmapfilename = locations['FSYSPATH']['polar_maps'] + '{0}_cmap.txt'.format(pdbi_ch1)
clickmapfile = open(clickmapfilename, 'w')
print(clickmap)
groups = set()
for sname, px, py in clickmap:
added_to_group = False
for g in groups:
not_in_this_group = False
for elem in g:
d = ((elem[1] - px) ** 2 + (elem[2] - py) ** 2) ** 0.5
if (d > 5):
not_in_this_group = True
break
if not not_in_this_group:
gg = set()
gg.add(g)
groups = groups - gg
g = set(g)
g.add((sname, px, py))
groups.add(frozenset(g))
added_to_group = True
break
if not added_to_group:
fs = set()
fs.add((sname, px, py))
groups.add(frozenset(fs))
clickmapfilename = locations['FSYSPATH']['polar_maps'] + '{0}_cmap.txt'.format(pdbi_ch1)
with open(clickmapfilename, 'w') as cmf:
cmf.write("[\n")
for ng, g in enumerate(groups):
cx = int(sum([n[1] for n in list(g)]) / len(list(g)))
cy = int(sum([n[2] for n in list(g)]) / len(list(g)))
ns = [n[0] for n in list(g)]
if ng == len(groups) - 1:
cmf.write("{{\"X\":{0},\"Y\":{1},\"radius\":3,\"chains\":\"{2}\"}}\n".format(cx, cy, "".join(
[x + "," for x in ns[:-1]]) + ns[-1]))
else:
cmf.write("{{\"X\":{0},\"Y\":{1},\"radius\":3,\"chains\":\"{2}\"}},\n".format(cx, cy, "".join(
[x + "," for x in ns[:-1]]) + ns[-1]))
cmf.write("]\n")
def scatterplots_parallel(an_locations, entry_list, ntms, pkl_fn, do_eps=False, do_pdf=False):
print("!About to open dictionary:", pkl_fn)
print("!Creating scatter plots:", do_pdf)
with open(pkl_fn, 'rb') as f:
packing = pickle.load(f)
(fig, (rect_scatter, rect_histx, rect_histy, binwidth), (si_tot, tm_tot, labels_tot)) = packing
labelorder = [x[0] for x in labels_tot]
fig.clf()
print("ENTRY LIST", entry_list, len(entry_list))
for i1, pdbi_ch1 in enumerate(entry_list):
fig.clf()
print("INIT CYCLE", i1, pdbi_ch1, time.time())
labid = labelorder.index(pdbi_ch1)
XX = si_tot[labid]
YY = tm_tot[labid]
labels = labels_tot[labid]
pdbi1, ch1 = pdbi_ch1.split('_')
layer_fn = an_locations['FSYSPATH']['densityscatter_figs'] + 'ds_' + pdbi_ch1 + '_layer.png'
figfilename = an_locations['FSYSPATH']['densityscatter_figs'] + 'ds_' + pdbi_ch1 + '.png'
ax = fig.add_axes(rect_scatter)
axHistx = fig.add_axes(rect_histx)
axHisty = fig.add_axes(rect_histy)
levels = np.array(
[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9,
0.95, 0.99])
cmap = copy.copy(cm.get_cmap('Blues'))
print("SCATTER", time.time())
plt.locator_params(nbins=6)
tt = ntms[pdbi1][ch1] # table[table["PDB1"] == pdbi]["NTM1"].iloc[0].astype(int).values[0]
ndif = []
for i2, pdbi_ch2 in enumerate(labels):
pdbi2, ch2 = pdbi_ch2.split('_')
ndif.append(ntms[pdbi2][ch2] - tt)
ax.set_axisbelow(True)
cmap.set_under("magenta")
cmap.set_over("yellow")
cmap.set_bad("red")
im = ax.scatter(XX, YY, c=(np.array(ndif) + 5) / 10, cmap='coolwarm_r', edgecolors='k', zorder=1)
im.set_clim(0.0, 1.0)
cbaxes = fig.add_axes([0.1, 0.01, 0.8, 0.03])
cmap = plt.get_cmap('coolwarm_r')
colorbar_index(ncolors=11, cmap=cmap, ntm=ntms[pdbi1][ch1], cax=cbaxes, horiz=True)
print("CANVAS DRAW", time.time())
fig.canvas.draw()
ax.set_xlabel("Sequence Identity")
ax.set_ylabel("Structure Similarity")
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
print("XHIST", time.time())
bins = np.arange(0, 1 + binwidth, binwidth)
plt.locator_params(nbins=4)
axHistx.hist(XX, bins=bins, color='pink')
axHistx.set_xlim(ax.get_xlim())
axHistx.set_xticks([])
print("YHIST", time.time())
plt.locator_params(nbins=4)
axHisty.hist(YY, bins=bins, orientation='horizontal', color='pink')
axHisty.set_ylim(ax.get_ylim())
axHisty.set_yticks([])
print("TRANSFORMS", time.time())
pix_xy = ax.transData.transform(np.vstack([XX, YY]).T)
pix_x, pix_y = pix_xy.T
width, height = fig.canvas.get_width_height()
pix_y = height - pix_y
snames = labels # [s for s in sorted(list(table[struct].keys()))]
clickmap = list(zip(snames, pix_x, pix_y))
clickmapfilename = an_locations['FSYSPATH']['densityscatter_maps'] + '{0}_cmap.txt'.format(pdbi_ch1)
clickmapfile = open(clickmapfilename, 'w')
print("GROUPS", time.time())
groups = set()
for sname, px, py in clickmap:
added_to_group = False
for g in groups:
not_in_this_group = False
for elem in g:
d = ((elem[1] - px) ** 2 + (elem[2] - py) ** 2) ** 0.5
if (d > 5):
not_in_this_group = True
break
if not not_in_this_group:
gg = set()
gg.add(g)
groups = groups - gg
g = set(g)
g.add((sname, px, py))
groups.add(frozenset(g))
added_to_group = True
break
if not added_to_group:
fs = set()
fs.add((sname, px, py))
groups.add(frozenset(fs))
print("WRITE CLICKMAP", time.time())
clickmapfile.write("[\n")
for ng, g in enumerate(groups):
cx = int(sum([n[1] for n in list(g)]) / len(list(g)))
cy = int(sum([n[2] for n in list(g)]) / len(list(g)))
ns = [n[0] for n in list(g)]
if ng == len(groups) - 1:
clickmapfile.write("{{\"X\":{0},\"Y\":{1:5d},\"radius\":3,\"chains\":\"{2}\"}}\n".format(cx, cy,
"".join(
[x + ","
for x in
ns[
:-1]]) +
ns[-1]))
else:
clickmapfile.write("{{\"X\":{0},\"Y\":{1:5d},\"radius\":3,\"chains\":\"{2}\"}},\n".format(cx, cy,
"".join(
[x + ","
for x in
ns[
:-1]]) +
ns[-1]))
clickmapfile.write("]\n")
clickmapfile.close()
fig.savefig(layer_fn, dpi=fig.dpi, transparent=True)
print("!Creating png:", layer_fn)
if do_eps:
fig.savefig(layer_fn[:-4] + '.eps', dpi=fig.dpi, transparent=True)
print("!Creating eps:", layer_fn[:-4] + '.eps')
if do_pdf:
fig.savefig(layer_fn[:-4] + '.pdf', dpi=fig.dpi, transparent=True)
print("!Creating pdf layer:", layer_fn[:-4] + '.pdf')
print("!, will need to be combined with background:", an_locations['SYSFILES']['background'])
plt.close(fig)
ax.remove()
print(" ".join(["composite", layer_fn, an_locations['SYSFILES']['background'], figfilename]))
msg = subprocess.run(["composite", layer_fn, an_locations['SYSFILES']['background'], figfilename],
stderr=subprocess.PIPE).stderr.decode('utf8')
print(msg)
print("PNG", figfilename)
os.remove(layer_fn)
def scatterplots_background(an_locations, entry_list, pkl_fn="sp.pkl", do_pdf=True):
N = len(entry_list)
# Extract TMscore and sequence identity AS METRICS
tm = [] # Includes self as first
si = []
labels = [] # Includes self as first
# Record all the TMscores from all entries
for pdbi_ch1 in entry_list:
tm.append([1])
si.append([1])
labels.append([pdbi_ch1])
totneigh_fn = an_locations['FSYSPATH']['totneighs'] + 'segment_' + pdbi_ch1 + '.txt'
with open(totneigh_fn) as totneigh_f:
for line in totneigh_f:
pdbi_ch2, seqid, tmscore = line.split()
tm[-1].append(float(tmscore))
si[-1].append(float(seqid))
labels[-1].append(pdbi_ch2)
xmin, xmax = 0, 1
ymin, ymax = 0, 1
xedges = np.mgrid[0:1:200j]
yedges = np.mgrid[0:1:200j]
H, xedges, yedges = np.histogram2d([item for sublist in tm for item in sublist],
[item for sublist in si for item in sublist], bins=(xedges, yedges))
# Take log of histogram, adjust -inf to 0
H[H == 0] = 0.00000001
H = np.log(H)
H[H > 1] = 1
# Build KDE on log-histogram, by creating a fake set of data
# on a 4-fold graph where the real graph is the lower left quadrant
DDx = []
DDy = []
for x in range(len(H)):
for y in range(len(H[x])):
for i in range(math.ceil(H[x][y])):
DDx.append(x / len(H))
DDy.append(y / len(H[x]))
DDx.append(2 - (x / len(H)))
DDy.append(y / len(H[x]))
DDx.append(x / len(H))
DDy.append(2 - (y / len(H[x])))
DDx.append(2 - (x / len(H)))
DDy.append(2 - (y / len(H[x])))
DDx = np.array(DDx)
DDy = np.array(DDy)
xmin = DDx.min()
xmax = DDx.max()
ymin = DDy.min()
ymax = DDy.max()
X, Y = np.mgrid[0:2:400j, 0:2:400j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([DDx, DDy])
kernel = scipy.stats.gaussian_kde(values)
Z = np.reshape(kernel(positions), X.shape)
Z = Z.T
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
binwidth = 0.05
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
fig_not = plt.figure(1, figsize=(8, 8))
ax_not = plt.axes(rect_scatter)
ax_not.set_xlim([0, 1])
ax_not.set_ylim([0, 1])
ZZ = np.array(Z)
levels = np.array(
[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95,
0.99])
cfset = ax_not.contourf(X, Y, ZZ, levels=levels, cmap="Blues")
cset = ax_not.contour(X, Y, ZZ, levels=levels, colors='k')
plt.locator_params(nbins=6)
ax_not.imshow(np.rot90(ZZ), cmap="Blues", extent=[0, 2, 0, 2], aspect=1)
ax_not.set_xlabel("Sequence Identity")
ax_not.set_ylabel("Structure Similarity")
fig_not.savefig(an_locations['SYSFILES']['background'], dpi=fig_not.dpi)
fig_not.savefig(an_locations['SYSFILES']['background'][:-4] + '.eps', dpi=fig_not.dpi)
fig_not.savefig(an_locations['SYSFILES']['background'][:-4] + '.pdf', dpi=fig_not.dpi)
packing = (fig_not, (rect_scatter, rect_histx, rect_histy, binwidth), (si, tm, labels))
with open(pkl_fn, 'wb') as f:
pickle.dump(packing, f)
plt.close(fig_not)
return
def scatterplots(options, locations, entry_list, ntms, an_locations, do_only=None, do_eps=False, do_pdf=False):
this_name = scatterplots.__name__
# Figure parameters
figsize = (8, 8)
points_per_inch = 72.272 # Conversion factor
clickmap = {} # Dict containing for each entry a list of 3 same-length lists with names, xcoord and ycoord of the pixels
N = len(entry_list)
# Extract TMscore and sequence identity AS METRICS
tm = [] # Includes self as first
si = []
labels = [] # Includes self as first
# Record all the TMscores from all entries
for pdbi_ch1 in entry_list:
tm.append([1])
si.append([1])
labels.append([pdbi_ch1])
totneigh_fn = an_locations['FSYSPATH']['totneighs'] + 'segment_' + pdbi_ch1 + '.txt'
with open(totneigh_fn) as totneigh_f:
for line in totneigh_f:
pdbi_ch2, seqid, tmscore = line.split()
tm[-1].append(float(tmscore))
si[-1].append(float(seqid))
labels[-1].append(pdbi_ch2)
xmin, xmax = 0, 1
ymin, ymax = 0, 1
xedges = np.mgrid[0:1:200j]
yedges = np.mgrid[0:1:200j]
H, xedges, yedges = np.histogram2d([item for sublist in tm for item in sublist],
[item for sublist in si for item in sublist], bins=(xedges, yedges))
# Take log of histogram, adjust -inf to 0
H[H == 0] = 0.00000001
H = np.log(H)
H[H > 1] = 1
# Build KDE on log-histogram, by creating a fake set of data
# on a 4-fold graph where the real graph is the lower left quadrant
DDx = []
DDy = []
for x in range(len(H)):
for y in range(len(H[x])):
for i in range(math.ceil(H[x][y])):
DDx.append(x / len(H))
DDy.append(y / len(H[x]))
DDx.append(2 - (x / len(H)))
DDy.append(y / len(H[x]))
DDx.append(x / len(H))
DDy.append(2 - (y / len(H[x])))
DDx.append(2 - (x / len(H)))
DDy.append(2 - (y / len(H[x])))
DDx = np.array(DDx)
DDy = np.array(DDy)
X, Y = np.mgrid[0:2:400j, 0:2:400j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([DDx, DDy])
kernel = scipy.stats.gaussian_kde(values)
Z = np.reshape(kernel(positions), X.shape)
Z = Z.T
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
binwidth = 0.05
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
fig_not = plt.figure(1, figsize=(8, 8))
ax_not = plt.axes(rect_scatter)
ax_not.set_xlim([0, 1])
ax_not.set_ylim([0, 1])
ZZ = np.array(Z)
levels = np.array(
[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95,
0.99])
cfset = ax_not.contourf(X, Y, ZZ, levels=levels, cmap="Blues")
cset = ax_not.contour(X, Y, ZZ, levels=levels, colors='k')
plt.locator_params(nbins=6)
ax_not.imshow(np.rot90(ZZ), cmap="Blues", extent=[0, 2, 0, 2], aspect=1)
ax_not.set_xlabel("Sequence Identity")
ax_not.set_ylabel("Structure Similarity")
fig_not.savefig(an_locations['FSYSPATH']['databasewide'] + 'densitylines.png', dpi=fig_not.dpi)
fig_not.savefig(an_locations['FSYSPATH']['databasewide'] + 'densitylines.eps', dpi=fig_not.dpi)
buf = io.BytesIO()
pickle.dump(fig_not, buf)
plt.close(fig_not)
for i1, pdbi_ch1 in enumerate(entry_list):
pdbi1, ch1 = pdbi_ch1.split('_')
figfilename = an_locations['FSYSPATH']['densityscatter_figs'] + 'ds_' + pdbi_ch1 + '.png'
ZZ = np.array(Z)
buf.seek(0)
fig = pickle.load(buf)
ax = plt.axes(rect_scatter)
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
levels = np.array(
[0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9,
0.95, 0.99])
cmap = copy.copy(cm.get_cmap('Blues'))
plt.locator_params(nbins=6)
tt = ntms[pdbi1][ch1] # table[table["PDB1"] == pdbi]["NTM1"].iloc[0].astype(int).values[0]
XX = si[i1]
YY = tm[i1]
ndif = []
for i2, pdbi_ch2 in enumerate(labels[i1]):
pdbi2, ch2 = pdbi_ch2.split('_')
ndif.append(ntms[pdbi2][ch2] - tt)
ax.set_axisbelow(True)
cmap.set_under("magenta")
cmap.set_over("yellow")
cmap.set_bad("red")
im = ax.scatter(XX, YY, c=(np.array(ndif) + 5) / 10, cmap='coolwarm_r', edgecolors='k', zorder=1)
im.set_clim(0.0, 1.0)
cbaxes = fig.add_axes([0.1, 0.01, 0.8, 0.03])
cmap = plt.get_cmap('coolwarm_r')
colorbar_index(ncolors=11, cmap=cmap, ntm=ntms[pdbi1][ch1], cax=cbaxes, horiz=True)
fig.canvas.draw()
ax.set_xlabel("Sequence Identity")
ax.set_ylabel("Structure Similarity")
bins = np.arange(0, 1 + binwidth, binwidth)
plt.locator_params(nbins=4)
axHistx.hist(XX, bins=bins, color='pink')
axHistx.set_xlim(ax.get_xlim())
axHistx.set_xticks([])
plt.locator_params(nbins=4)
axHisty.hist(YY, bins=bins, orientation='horizontal', color='pink')
axHisty.set_ylim(ax.get_ylim())
axHisty.set_yticks([])
pix_xy = ax.transData.transform(np.vstack([XX, YY]).T)
pix_x, pix_y = pix_xy.T
width, height = fig.canvas.get_width_height()
pix_y = height - pix_y
snames = labels[i1] # [s for s in sorted(list(table[struct].keys()))]
clickmap = list(zip(snames, pix_x, pix_y))
clickmapfilename = an_locations['FSYSPATH']['densityscatter_maps'] + '{0}_cmap.txt'.format(pdbi_ch1)
clickmapfile = open(clickmapfilename, 'w')
groups = set()
for sname, px, py in clickmap:
added_to_group = False
for g in groups:
not_in_this_group = False
for elem in g:
d = ((elem[1] - px) ** 2 + (elem[2] - py) ** 2) ** 0.5
if (d > 5):
not_in_this_group = True
break
if not not_in_this_group:
gg = set()
gg.add(g)
groups = groups - gg
g = set(g)
g.add((sname, px, py))
groups.add(frozenset(g))
added_to_group = True
break
if not added_to_group:
# print("New group created!")
fs = set()
fs.add((sname, px, py))
groups.add(frozenset(fs))
clickmapfile.write("[\n")
for ng, g in enumerate(groups):
cx = int(sum([n[1] for n in list(g)]) / len(list(g)))
cy = int(sum([n[2] for n in list(g)]) / len(list(g)))
ns = [n[0] for n in list(g)]
if ng == len(groups) - 1:
clickmapfile.write("{{\"X\":{0},\"Y\":{1:5d},\"radius\":3,\"chains\":\"{2}\"}}\n".format(cx, cy,
"".join(
[x + ","
for x in
ns[
:-1]]) +
ns[-1]))
else:
clickmapfile.write("{{\"X\":{0},\"Y\":{1:5d},\"radius\":3,\"chains\":\"{2}\"}},\n".format(cx, cy,
"".join(
[x + ","
for x in
ns[
:-1]]) +
ns[-1]))
clickmapfile.write("]\n")
clickmapfile.close()
fig.savefig(figfilename, dpi=fig.dpi)
if do_eps:
fig.savefig(figfilename[:-4] + '.eps', dpi=fig.dpi)
if do_pdf:
fig.savefig(figfilename[:-4] + '.pdf', dpi=fig.dpi)
plt.close(fig)
def savitzky_golay(y, window_size, order, deriv=0, rate=1):
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
if window_size % 2 != 1 or window_size < 1:
window_size += 1
if window_size < order + 2:
raise TypeError("window_size is too small for the polynomials order")
order_range = range(order + 1)
half_window = (window_size - 1) // 2
# precompute coefficients
b = np.mat([[k ** i for i in order_range] for k in range(-half_window, half_window + 1)])
m = np.linalg.pinv(b).A[deriv] * rate ** deriv * factorial(deriv)
# pad the signal at the extremes with
# values taken from the signal itself
firstvals = y[0] - np.abs(y[1:half_window + 1][::-1] - y[0])
lastvals = y[-1] + np.abs(y[-half_window - 1:-1][::-1] - y[-1])
y = np.concatenate((firstvals, y, lastvals))
return np.convolve(m[::-1], y, mode='valid')
def smoothen(l, k):
k1 = int(k / 2)
k2 = k - k1
newl = [l[0]] * k1 + l + [l[-1]] * k2
res = []
for i in range(len(l)):
res.append(sum(newl[i:i + k]) / k)
return res
def straln_profiles(locations, entry_list, str_data, an_locations, do_eps=False, do_pdf=False):
import matplotlib
cmap = matplotlib.cm.get_cmap('hsv')
colors = colorblind_palette()
print("COLORS", colors)
print(entry_list)
dgts = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9'}
for pdbi_ch1 in entry_list:
if os.path.exists(an_locations['FSYSPATH']['distributions_figs'] + 'distr_' + pdbi_ch1 + '.png'):
continue
pdbi1, ch1 = pdbi_ch1[:4], pdbi_ch1[5]
ref_ch1 = ch1
if ch1 in str_data[pdbi1]['ENCOMPASS']['structure']['redundant_chains']:
ref_ch1 = str_data[pdbi1]['ENCOMPASS']['structure']['redundant_chains'][ch1]
straln_fn = an_locations['FSYSPATH']['distributions_data'] + '/distr_' + pdbi1 + "_" + ref_ch1 + '.txt'
else:
straln_fn = an_locations['FSYSPATH']['distributions_data'] + '/distr_' + pdbi_ch1 + '.txt'
anc = {}
print(straln_fn)
if not os.path.exists(straln_fn):
print(pdbi_ch1, "NOT THERE", straln_fn)
ref_ch1 = ch1
straln_fn = an_locations['FSYSPATH']['distributions_data'] + '/distr_' + pdbi_ch1 + '.txt'
if not os.path.exists(straln_fn):
print(pdbi_ch1, "NOT THERE AGAIN", straln_fn)
continue
with open(straln_fn) as straln_f:
for line in straln_f:
if not line.strip():
continue
fields = line.split()
if 'BEGIN' in line:
pdbi_ch2 = fields[4]
if pdbi_ch2 not in anc:
anc[pdbi_ch2] = {}
elif 'END' in line:
continue
else:
compl = ''
if fields[0][-1] not in dgts:
nri = int(fields[0][:-1])
compl = fields[0][-1]
else:
compl = ''
nri = int(fields[0])
anc[pdbi_ch2][(nri, compl)] = float(fields[1])
print(pdbi_ch1, "LEN ANC", len(anc))
polarc_fn = an_locations['FSYSPATH']['polar_data'] + '{0}_neigh.txt'.format(pdbi_ch1)
coords = {}
pdbis = set()
with open(polarc_fn) as polarc_f:
for line in polarc_f:
if not line.strip() or line.strip().startswith('#'):
continue
pdbi_ch2, rho, theta, _ = line.split()
if float(rho) > 0.001 and pdbi_ch2 in anc:
coords[pdbi_ch2] = pol2cart(float(rho), float(theta))
pdbis.add(pdbi_ch2)
if not pdbis:
print(pdbi_ch1, "ALL ENTRIES ARE IDENTICAL", polarc_fn)
continue
print(pdbi_ch1, "POLARC", polarc_fn)
print(pdbi_ch1, "PDBIS", pdbis)
Nall = len(pdbis)
groups = []
CLUSTER_RADIUS = 0.3
X = []
X_labels = []
for pdbi_ch2 in sorted(list(pdbis)):
X_labels.append(pdbi_ch2)
X.append(coords[pdbi_ch2])
if len(X_labels) > 1:
clustering = AgglomerativeClustering(n_clusters=None, distance_threshold=CLUSTER_RADIUS,
compute_full_tree=True).fit(X)
for ix in range(max(clustering.labels_) + 1):
group = []
for iix, il in enumerate(clustering.labels_):
if il == ix:
group.append(X_labels[iix])
groups.append(group)
elif len(X_labels) == 1:
group = X_labels[:]
groups.append(group)
lines = []
ich1 = str_data[pdbi1]['ENCOMPASS']['structure']['kchains'].index(ref_ch1)
iress = []
for x in str_data[pdbi1]['ENCOMPASS']['structure']['chains'][ich1]['residues']:
iress.append((x[0][0], x[0][1]))
for group in groups:
# print("GROUP", group)
ganc = {}
norm = {}
for pdbi_ch2 in group:
for ires in anc[pdbi_ch2]:
if ires not in ganc:
ganc[ires] = 0
norm[ires] = 0
ganc[ires] += anc[pdbi_ch2][ires]
norm[ires] += 1
for ires in ganc: