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 = []