def mi_reshape(X, y, seq_len = 1):
if len(X.shape) == 3:
X = np_reshape(X, (int((X.shape[0] * X.shape[1])/seq_len), seq_len, X.shape[2]))
else:
X = np_reshape(X, (int(X.shape[0]/seq_len), seq_len, X.shape[1]))
if not y is None:
if len(y.shape) == 3:
y = np_reshape(y, (int((y.shape[0] * y.shape[1])/seq_len), seq_len, y.shape[2]))
else:
if seq_len != 1:
y = np_reshape(y, (int(y.shape[0]/seq_len), seq_len, y.shape[1]))
print(X.shape, y.shape)
else:
print(X.shape)
return [X, y]
def pca(self, data_matrix):
"""Perform PCA.
Principal components are given in self.pca,
and the variance in self.variance.
Parameters
----------
data_matrix : list of lists
List of tetranucleotide signatures
"""
cols = len(data_matrix[0])
data_matrix = np_reshape(np_array(data_matrix), (len(data_matrix), cols))
pca = PCA()
pc, variance = pca.pca_matrix(data_matrix, 3, bCenter=True, bScale=False)
# ensure pc matrix has at least 3 dimensions
if pc.shape[1] == 1:
pc = np_append(pc, np_zeros((pc.shape[0], 2)), 1)
variance = np_append(variance[0], np_ones(2))
elif pc.shape[1] == 2:
pc = np_append(pc, np_zeros((pc.shape[0], 1)), 1)
variance = np_append(variance[0:2], np_ones(1))
return pc, variance
def pca(self, data_matrix):
"""Perform PCA.
Principal components are given in self.pca,
and the variance in self.variance.
Parameters
----------
data_matrix : list of lists
List of tetranucleotide signatures
"""
cols = len(data_matrix[0])
data_matrix = np_reshape(np_array(data_matrix),
(len(data_matrix), cols))
pca = PCA()
pc, variance = pca.pca_matrix(data_matrix,
3,
bCenter=True,
bScale=False)
# ensure pc matrix has at least 3 dimensions
if pc.shape[1] == 1:
pc = np_append(pc, np_zeros((pc.shape[0], 2)), 1)
variance = np_append(variance[0], np_ones(2))
elif pc.shape[1] == 2:
pc = np_append(pc, np_zeros((pc.shape[0], 1)), 1)
variance = np_append(variance[0:2], np_ones(1))
return pc, variance
def mi_reshape_probs(probs, seq_len = 1):
if len(probs.shape) == 3:
if seq_len != probs.shape[1]:
print('NOTA: La dimensión Seq_Len de probs NO coincide con el param. seq_len!')
probs = np_reshape(probs, (probs.shape[0] * probs.shape[1], probs.shape[2]))
print(probs.shape)
return(probs)
def kpts_from_file(self, **kwargs):
from numpy import reshape as np_reshape
from numpy import fromfile as np_fromfile
from numpy import ndarray as ndarray
# Read k-points from file (obsolete):
nkTetra = kwargs['nkTetra']
kpt_filename = "symmetries/" + self.prefix + ".kcartesian_" + str(
nkTetra)
kcartesian = np_fromfile(kpt_filename, sep=' ')
# Get reciprocal lattice vectors:
# Pymatgen. lattice. reciprocal_lattice(self)
# Get inverse matrix, to then convert to recp.
# Note that cartesian coordinates are in angstrom:
bohr2ang = 0.529177249
reciprocal_lattice = self.structure.lattice.reciprocal_lattice
invM = reciprocal_lattice.inv_matrix / bohr2ang
# Check k-points in file are OK
nk = kcartesian.size / 3
if (nk != nkTetra):
print("%i k-points found in file %s.\nExpecting %i \n" %
(nk, nkTetra))
exit(1)
kcartesian = np_reshape(kcartesian, (nk, 3))
kpt = ndarray(shape=(nkTetra, 3), dtype=float)
for ik in range(nkTetra):
kk = kcartesian[ik][:]
kred = dot(kk, invM)
kpt[ik][:] = round(kred, 6)
def read_input(self, sample_name: str, start_idx: int, end_idx: int):
self.__check_indexes(start_idx, end_idx)
full_transform = self.read_full_transform(sample_name)
if full_transform.shape[0] < self.time_size:
return np_array([])
x = self.normalize_input(full_transform[start_idx:end_idx, :])
return np_reshape(x, (1, self.time_size, self.freq_size, 1))
def plotRegion(self, px, py, pz, fileName="", tag="", column=False):
"""Plot the region surrounding a point """
import matplotlib as mpl
disp_vals = np_array([])
disp_cols = np_array([])
num_points = 0
# plot all points within span
(z_lower, z_upper) = self.makeCoordRanges(pz, self.span)
if column:
z_lower = 0
z_upper = self.PM.scaleFactor - 1
(x_lower, x_upper) = self.makeCoordRanges(px, self.span)
(y_lower, y_upper) = self.makeCoordRanges(py, self.span)
for z in range(z_lower, z_upper):
realz = self.PM.scaleFactor - z - 1
for x in range(x_lower, x_upper):
for y in range(y_lower, y_upper):
if (x, y, realz) in self.im2RowIndicies:
for row_index in self.im2RowIndicies[(x, y, realz)]:
if (
row_index not in self.PM.binnedRowIndicies
and row_index not in self.PM.restrictedRowIndicies
):
num_points += 1
disp_vals = np_append(disp_vals, self.PM.transformedCP[row_index])
disp_cols = np_append(disp_cols, self.PM.contigColours[row_index])
# make a black mark at the max values
small_span = self.span / 2
(x_lower, x_upper) = self.makeCoordRanges(px, small_span)
(y_lower, y_upper) = self.makeCoordRanges(py, small_span)
(z_lower, z_upper) = self.makeCoordRanges(pz, small_span)
for z in range(z_lower, z_upper):
realz = self.PM.scaleFactor - z - 1
for x in range(x_lower, x_upper):
for y in range(y_lower, y_upper):
if (x, y, realz) in self.im2RowIndicies:
for row_index in self.im2RowIndicies[(x, y, realz)]:
if (
row_index not in self.PM.binnedRowIndicies
and row_index not in self.PM.restrictedRowIndicies
):
num_points += 1
disp_vals = np_append(disp_vals, self.PM.transformedCP[row_index])
disp_cols = np_append(disp_cols, htr(0, 0, 0))
# reshape
disp_vals = np_reshape(disp_vals, (num_points, 3))
disp_cols = np_reshape(disp_cols, (num_points, 3))
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
cm = mpl.colors.LinearSegmentedColormap("my_colormap", disp_cols, 1024)
result = ax.scatter(
disp_vals[:, 0], disp_vals[:, 1], disp_vals[:, 2], edgecolors=disp_cols, c=disp_cols, cmap=cm, marker="."
)
title = str.join(" ", ["Focus at: (", str(px), str(py), str(self.PM.scaleFactor - pz - 1), ")\n", tag])
plt.title(title)
if fileName != "":
fig.set_size_inches(6, 6)
plt.savefig(fileName, dpi=300)
elif show:
plt.show()
plt.close(fig)
del fig
def renderTransCPData(self,
fileName="",
show=True,
elev=45,
azim=45,
all=False,
showAxis=False,
primaryWidth=12,
primarySpace=3,
dpi=300,
format='png',
fig=None,
highlight=None,
restrictedBids=[],
alpha=1,
ignoreContigLengths=False):
"""Plot transformed data in 3D"""
del_fig = False
if (fig is None):
fig = plt.figure()
del_fig = True
else:
plt.clf()
if (all):
myAXINFO = {
'x': {
'i': 0,
'tickdir': 1,
'juggled': (1, 0, 2),
'color': (0, 0, 0, 0, 0)
},
'y': {
'i': 1,
'tickdir': 0,
'juggled': (0, 1, 2),
'color': (0, 0, 0, 0, 0)
},
'z': {
'i': 2,
'tickdir': 0,
'juggled': (0, 2, 1),
'color': (0, 0, 0, 0, 0)
},
}
ax = fig.add_subplot(131, projection='3d')
sc = ax.scatter(self.transformedCP[:, 0],
self.transformedCP[:, 1],
self.transformedCP[:, 2],
edgecolors='k',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args: None # disable depth transparency effect
ax.azim = 0
ax.elev = 0
ax.set_xlim3d(0, self.scaleFactor)
ax.set_ylim3d(0, self.scaleFactor)
ax.set_zlim3d(0, self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
ax = fig.add_subplot(132, projection='3d')
sc = ax.scatter(self.transformedCP[:, 0],
self.transformedCP[:, 1],
self.transformedCP[:, 2],
edgecolors='k',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args: None # disable depth transparency effect
ax.azim = 90
ax.elev = 0
ax.set_xlim3d(0, self.scaleFactor)
ax.set_ylim3d(0, self.scaleFactor)
ax.set_zlim3d(0, self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
ax = fig.add_subplot(133, projection='3d')
sc = ax.scatter(self.transformedCP[:, 0],
self.transformedCP[:, 1],
self.transformedCP[:, 2],
edgecolors='k',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args: None # disable depth transparency effect
ax.azim = 0
ax.elev = 90
ax.set_xlim3d(0, self.scaleFactor)
ax.set_ylim3d(0, self.scaleFactor)
ax.set_zlim3d(0, self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
else:
ax = fig.add_subplot(111, projection='3d')
if len(restrictedBids) == 0:
if highlight is None:
print("BF:", np_shape(self.transformedCP))
if ignoreContigLengths:
sc = ax.scatter(self.transformedCP[:, 0],
self.transformedCP[:, 1],
self.transformedCP[:, 2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
s=10.,
vmin=0.0,
vmax=1.0,
marker='.')
else:
sc = ax.scatter(self.transformedCP[:, 0],
self.transformedCP[:, 1],
self.transformedCP[:, 2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=np_sqrt(self.contigLengths),
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args: None # disable depth transparency effect
else:
#draw the opaque guys first
"""
sc = ax.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=100.,
marker='s',
alpha=alpha)
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
"""
# now replot the highlighted guys
disp_vals = np_array([])
disp_GCs = np_array([])
thrower = {}
hide_vals = np_array([])
hide_GCs = np_array([])
num_points = 0
for bin in highlight:
for row_index in bin.rowIndices:
num_points += 1
disp_vals = np_append(
disp_vals, self.transformedCP[row_index])
disp_GCs = np_append(disp_GCs,
self.contigGCs[row_index])
thrower[row_index] = False
# reshape
disp_vals = np_reshape(disp_vals, (num_points, 3))
num_points = 0
for i in range(len(self.indices)):
try:
thrower[i]
except KeyError:
num_points += 1
hide_vals = np_append(hide_vals,
self.transformedCP[i])
hide_GCs = np_append(hide_GCs, self.contigGCs[i])
# reshape
hide_vals = np_reshape(hide_vals, (num_points, 3))
sc = ax.scatter(hide_vals[:, 0],
hide_vals[:, 1],
hide_vals[:, 2],
edgecolors='none',
c=hide_GCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=100.,
marker='s',
alpha=alpha)
sc.set_edgecolors = sc.set_facecolors = lambda *args: None # disable depth transparency effect
sc = ax.scatter(disp_vals[:, 0],
disp_vals[:, 1],
disp_vals[:, 2],
edgecolors='none',
c=disp_GCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=10.,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args: None # disable depth transparency effect
print(np_shape(disp_vals), np_shape(hide_vals),
np_shape(self.transformedCP))
# render color bar
cbar = plt.colorbar(sc, shrink=0.5)
cbar.ax.tick_params()
cbar.ax.set_title("% GC", size=10)
cbar.set_ticks([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
cbar.ax.set_ylim([0.15, 0.85])
mungeCbar(cbar)
else:
r_trans = np_array([])
r_cols = np_array([])
num_added = 0
for i in range(len(self.indices)):
if self.binIds[i] not in restrictedBids:
r_trans = np_append(r_trans, self.transformedCP[i])
r_cols = np_append(r_cols, self.contigGCs[i])
num_added += 1
r_trans = np_reshape(r_trans, (num_added, 3))
print(np_shape(r_trans))
#r_cols = np_reshape(r_cols, (num_added,3))
sc = ax.scatter(r_trans[:, 0],
r_trans[:, 1],
r_trans[:, 2],
edgecolors='none',
c=r_cols,
cmap=self.colorMapGC,
s=10.,
vmin=0.0,
vmax=1.0,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args: None # disable depth transparency effect
# render color bar
cbar = plt.colorbar(sc, shrink=0.5)
cbar.ax.tick_params()
cbar.ax.set_title("% GC", size=10)
cbar.set_ticks([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
cbar.ax.set_ylim([0.15, 0.85])
mungeCbar(cbar)
ax.azim = azim
ax.elev = elev
ax.set_xlim3d(0, self.scaleFactor)
ax.set_ylim3d(0, self.scaleFactor)
ax.set_zlim3d(0, self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
if (not showAxis):
ax.set_axis_off()
if (fileName != ""):
try:
if (all):
fig.set_size_inches(3 * primaryWidth + 2 * primarySpace,
primaryWidth)
else:
fig.set_size_inches(primaryWidth, primaryWidth)
plt.savefig(fileName, dpi=dpi, format=format)
except:
print("Error saving image", fileName, exc_info()[0])
raise
elif (show):
try:
plt.show()
except:
print("Error showing image", exc_info()[0])
raise
if del_fig:
plt.close(fig)
del fig
def renderTransCPData(self,
fileName="",
show=True,
elev=45,
azim=45,
all=False,
showAxis=False,
primaryWidth=12,
primarySpace=3,
dpi=300,
format='png',
fig=None,
highlight=None,
restrictedBids=[],
alpha=1,
ignoreContigLengths=False):
"""Plot transformed data in 3D"""
del_fig = False
if(fig is None):
fig = plt.figure()
del_fig = True
else:
plt.clf()
if(all):
myAXINFO = {
'x': {'i': 0, 'tickdir': 1, 'juggled': (1, 0, 2),
'color': (0, 0, 0, 0, 0)},
'y': {'i': 1, 'tickdir': 0, 'juggled': (0, 1, 2),
'color': (0, 0, 0, 0, 0)},
'z': {'i': 2, 'tickdir': 0, 'juggled': (0, 2, 1),
'color': (0, 0, 0, 0, 0)},
}
ax = fig.add_subplot(131, projection='3d')
sc = ax.scatter(self.transformedCP[:,0], self.transformedCP[:,1], self.transformedCP[:,2], edgecolors='k', c=self.contigGCs, cmap=self.colorMapGC, vmin=0.0, vmax=1.0, marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
ax.azim = 0
ax.elev = 0
ax.set_xlim3d(0,self.scaleFactor)
ax.set_ylim3d(0,self.scaleFactor)
ax.set_zlim3d(0,self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
ax = fig.add_subplot(132, projection='3d')
sc = ax.scatter(self.transformedCP[:,0], self.transformedCP[:,1], self.transformedCP[:,2], edgecolors='k', c=self.contigGCs, cmap=self.colorMapGC, vmin=0.0, vmax=1.0, marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
ax.azim = 90
ax.elev = 0
ax.set_xlim3d(0,self.scaleFactor)
ax.set_ylim3d(0,self.scaleFactor)
ax.set_zlim3d(0,self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
ax = fig.add_subplot(133, projection='3d')
sc = ax.scatter(self.transformedCP[:,0], self.transformedCP[:,1], self.transformedCP[:,2], edgecolors='k', c=self.contigGCs, cmap=self.colorMapGC, vmin=0.0, vmax=1.0, marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
ax.azim = 0
ax.elev = 90
ax.set_xlim3d(0,self.scaleFactor)
ax.set_ylim3d(0,self.scaleFactor)
ax.set_zlim3d(0,self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
else:
ax = fig.add_subplot(111, projection='3d')
if len(restrictedBids) == 0:
if highlight is None:
print "BF:", np_shape(self.transformedCP)
if ignoreContigLengths:
sc = ax.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
s=10.,
vmin=0.0,
vmax=1.0,
marker='.')
else:
sc = ax.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=np_sqrt(self.contigLengths),
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
else:
#draw the opaque guys first
"""
sc = ax.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=100.,
marker='s',
alpha=alpha)
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
"""
# now replot the highlighted guys
disp_vals = np_array([])
disp_GCs = np_array([])
thrower = {}
hide_vals = np_array([])
hide_GCs = np_array([])
num_points = 0
for bin in highlight:
for row_index in bin.rowIndices:
num_points += 1
disp_vals = np_append(disp_vals, self.transformedCP[row_index])
disp_GCs = np_append(disp_GCs, self.contigGCs[row_index])
thrower[row_index] = False
# reshape
disp_vals = np_reshape(disp_vals, (num_points, 3))
num_points = 0
for i in range(len(self.indices)):
try:
thrower[i]
except KeyError:
num_points += 1
hide_vals = np_append(hide_vals, self.transformedCP[i])
hide_GCs = np_append(hide_GCs, self.contigGCs[i])
# reshape
hide_vals = np_reshape(hide_vals, (num_points, 3))
sc = ax.scatter(hide_vals[:,0],
hide_vals[:,1],
hide_vals[:,2],
edgecolors='none',
c=hide_GCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=100.,
marker='s',
alpha=alpha)
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
sc = ax.scatter(disp_vals[:,0],
disp_vals[:,1],
disp_vals[:,2],
edgecolors='none',
c=disp_GCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=10.,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
print np_shape(disp_vals), np_shape(hide_vals), np_shape(self.transformedCP)
# render color bar
cbar = plt.colorbar(sc, shrink=0.5)
cbar.ax.tick_params()
cbar.ax.set_title("% GC", size=10)
cbar.set_ticks([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
cbar.ax.set_ylim([0.15, 0.85])
mungeCbar(cbar)
else:
r_trans = np_array([])
r_cols=np_array([])
num_added = 0
for i in range(len(self.indices)):
if self.binIds[i] not in restrictedBids:
r_trans = np_append(r_trans, self.transformedCP[i])
r_cols = np_append(r_cols, self.contigGCs[i])
num_added += 1
r_trans = np_reshape(r_trans, (num_added,3))
print np_shape(r_trans)
#r_cols = np_reshape(r_cols, (num_added,3))
sc = ax.scatter(r_trans[:,0],
r_trans[:,1],
r_trans[:,2],
edgecolors='none',
c=r_cols,
cmap=self.colorMapGC,
s=10.,
vmin=0.0,
vmax=1.0,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
# render color bar
cbar = plt.colorbar(sc, shrink=0.5)
cbar.ax.tick_params()
cbar.ax.set_title("% GC", size=10)
cbar.set_ticks([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
cbar.ax.set_ylim([0.15, 0.85])
mungeCbar(cbar)
ax.azim = azim
ax.elev = elev
ax.set_xlim3d(0,self.scaleFactor)
ax.set_ylim3d(0,self.scaleFactor)
ax.set_zlim3d(0,self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
if(not showAxis):
ax.set_axis_off()
if(fileName != ""):
try:
if(all):
fig.set_size_inches(3*primaryWidth+2*primarySpace,primaryWidth)
else:
fig.set_size_inches(primaryWidth,primaryWidth)
plt.savefig(fileName,dpi=dpi,format=format)
except:
print "Error saving image",fileName, exc_info()[0]
raise
elif(show):
try:
plt.show()
except:
print "Error showing image", exc_info()[0]
raise
if del_fig:
plt.close(fig)
del fig
def r3_dnn_apply_keras(target_dirname,
old_stft_obj=None,
cuda=False,
saving_to_disk=True):
LOGGER.info(
'{}: r3: Denoising original stft with neural network model...'.format(
target_dirname))
'''
r3_dnn_apply takes an old_stft object (or side effect load from disk)
and saves a new_stft object
'''
scan_battery_dirname = os_path_dirname(target_dirname)
model_dirname = os_path_dirname(os_path_dirname(scan_battery_dirname))
# load stft data
if old_stft_obj is None:
old_stft_fpath = os_path_join(target_dirname, 'old_stft.mat')
with h5py_File(old_stft_fpath, 'r') as f:
stft = np_concatenate(
[f['old_stft_real'][:], f['old_stft_imag'][:]], axis=1)
else:
stft = np_concatenate(
[old_stft_obj['old_stft_real'], old_stft_obj['old_stft_imag']],
axis=1)
N_beams, N_elements_2, N_segments, N_fft = stft.shape
N_elements = N_elements_2 // 2
# combine stft_real and stft_imag
# move element position axis
stft = np_moveaxis(stft, 1, 2) # TODO: Duplicate?
# reshape the to flatten first two axes
stft = np_reshape(
stft, [N_beams * N_segments, N_elements_2, N_fft]) # TODO: Duplicate?
# process stft with networks
k_mask = list(range(3, 6))
for frequency in k_mask:
process_each_frequency_keras(model_dirname, stft, frequency)
# reshape the stft data
stft = np_reshape(
stft, [N_beams, N_segments, N_elements_2, N_fft]) # TODO: Duplicate?
# set zero outside analysis frequency range
discard_mask = np_ones_like(stft, dtype=bool)
discard_mask[:, :, :, k_mask] = False # pylint: disable=E1137
stft[discard_mask] = 0
del discard_mask
# mirror data to negative frequencies using conjugate symmetry
end_index = N_fft // 2
stft[:, :, :, end_index + 1:] = np_flip(stft[:, :, :, 1:end_index], axis=3)
stft[:, :, N_elements:2 * N_elements, end_index +
1:] = -1 * stft[:, :, N_elements:2 * N_elements, end_index + 1:]
# move element position axis
stft = np_moveaxis(stft, 1, 2) # TODO: Duplicate?
# change variable names
# new_stft_real = stft[:, :N_elements, :, :]
new_stft_real = stft[:, :N_elements, :, :].transpose()
# new_stft_imag = stft[:, N_elements:, :, :]
new_stft_imag = stft[:, N_elements:, :, :].transpose()
del stft
# change dimensions
# new_stft_real = new_stft_real.transpose()
# new_stft_imag = new_stft_imag.transpose()
# save new stft data
new_stft_obj = {
'new_stft_real': new_stft_real,
'new_stft_imag': new_stft_imag
}
if saving_to_disk is True:
new_stft_fname = os_path_join(target_dirname, 'new_stft.mat')
savemat(new_stft_fname, new_stft_obj)
LOGGER.info('{}: r3 Done.'.format(target_dirname))
return new_stft_obj
