def plotSolnAndGrad(self, plt, solVec, title=""):
plt.set_title(title)
# plt.set_xlabel('x',size=14,weight='bold')
# plt.set_ylabel('y',size=14,weight='bold')
plt.set_aspect('equal')
plt.set_xlim(-0.1, 5.1)
plt.set_ylim(-0.1, 1.2)
xy = np.asarray(self.mesh.vertices)
tci = tri.CubicTriInterpolator(self.mesh.dtri, solVec)
(Ex, Ey) = tci.gradient(self.mesh.dtri.x, self.mesh.dtri.y)
E_norm = np.sqrt(Ex**2 + Ey**2)
vals = plt.tricontourf(self.mesh.dtri, solVec, cmap="jet")
plt.quiver(self.mesh.dtri.x,
self.mesh.dtri.y,
-Ex / E_norm,
-Ey / E_norm,
units='xy',
scale=20.,
zorder=3,
color='blue',
width=0.002,
headwidth=2.,
headlength=2.)
return vals
def plotMesh(self, plt, title=""):
plt.set_title("Mesh")
# plt.set_xlabel('x',size=14,weight='bold')
# plt.set_ylabel('y',size=14,weight='bold')
plt.set_aspect('equal');
plt.set_xlim(-0.1,5.1); plt.set_ylim(-0.1,1.2);
xy = np.asarray(self.mesh.vertices);
vals=plt.triplot(xy[:,0],xy[:,1],self.mesh.elements,'b-',linewidth=0.5);
return vals
def plotSoln(self, plt, solVec, title=""):
plt.set_title(title)
# plt.set_xlabel('x',size=14,weight='bold')
# plt.set_ylabel('y',size=14,weight='bold')
plt.set_aspect('equal')
plt.set_xlim(-0.1, 5.1)
plt.set_ylim(-0.1, 1.2)
xy = np.asarray(self.mesh.vertices)
if xy.size < 10000:
plt.triplot(xy[:, 0],
xy[:, 1],
self.mesh.elements,
'b-',
linewidth=0.5)
vals = plt.tricontourf(self.mesh.dtri, solVec, cmap="jet")
return vals
cv.append(row)
mean = []
for row in cv_data[2]:
mean.append(math.log10(row))
scaled_cv = []
for val in cv:
scaled_cv.append(math.log10(val ** 2))
xs = mean
ys = scaled_cv
resolution = 250
neighbours = 16
if neighbours == 0:
plt.plot(xs, ys, 'k.', markersize=2)
plt.set_aspect('equal')
plt.title("Scatter Plot")
else:
im, extent = nearest_neighbours(xs, ys, resolution, neighbours)
plt.imshow(im, origin='lower', extent=extent, cmap=cm.bone)
plt.title("Raw Transcript counts from single-cells")
plt.xlim(extent[0], extent[1])
plt.ylim(extent[2], extent[3])
plt.show()
rank_norm_data = pd.read_csv('./data/rna/raw_pagoda.csv', header=None, index_col=False)
mean_express = []
for row in rank_norm_data[0]:
mean_express.append(math.log10(row))
var = []