我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用matplotlib.mlab.griddata()。
def interpolate_apertures(self, aperture_centers, aperture_means): """ This function ... :param aperture_centers: :param aperture_means: :return: """ # Inform the user log.info("Interpolating between the mean values of each aperture to fill the sky frame ...") x_values = np.array([center.x for center in aperture_centers]) y_values = np.array([center.y for center in aperture_centers]) x_ticks = np.arange(0, self.frame.xsize, 1) y_ticks = np.arange(0, self.frame.ysize, 1) z_grid = mlab.griddata(x_values, y_values, aperture_means, x_ticks, y_ticks) # Set the sky frame self.sky = Frame(z_grid) # -----------------------------------------------------------------
def plot_interpolated(self, aperture_centers, aperture_means): """ This function ... :param aperture_centers: :param aperture_means: :return: """ x_values = np.array([center.x for center in aperture_centers]) y_values = np.array([center.y for center in aperture_centers]) x_ticks = np.arange(0, self.frame.xsize, 1) y_ticks = np.arange(0, self.frame.ysize, 1) z_grid = mlab.griddata(x_values, y_values, aperture_means, x_ticks, y_ticks) self.sky = Frame(z_grid) from matplotlib.backends import backend_agg as agg from matplotlib import cm # plot #fig = Figure() # create the figure fig = plt.figure() agg.FigureCanvasAgg(fig) # attach the rasterizer ax = fig.add_subplot(1, 1, 1) # make axes to plot on ax.set_title("Interpolated Contour Plot of Experimental Data") ax.set_xlabel("X") ax.set_ylabel("Y") cmap = cm.get_cmap("hot") # get the "hot" color map contourset = ax.contourf(x_ticks, y_ticks, z_grid, 10, cmap=cmap) cbar = fig.colorbar(contourset) cbar.set_ticks([0, 100]) fig.axes[-1].set_ylabel("Z") # last axes instance is the colorbar plt.show() # -----------------------------------------------------------------
def contour(self,title='',cbartitle = '',model=[], zmax = None, zmin = None, filename = None, resolution = 1, unit_str = '', bar = True): """ Returns a figure with contourplot of 2D spatial data. Insert filename to save the figure as an image. Increase resolution to increase detail of interpolated data (<1 to decrease)""" font = {'weight' : 'medium', 'size' : 22} xi = np.linspace(min(self.data.x), max(self.data.x),len(set(self.data.x))*resolution) yi = np.linspace(min(self.data.y), max(self.data.y),len(set(self.data.y))*resolution) zi = ml.griddata(self.data.x, self.data.y, self.data.v.interpolate(), xi, yi,interp='linear') fig = plt.figure() plt.rc('font', **font) plt.title(title) plt.contour(xi, yi, zi, 15, linewidths = 0, cmap=plt.cm.bone) plt.pcolormesh(xi, yi, zi, cmap = plt.get_cmap('rainbow'),vmax = zmax, vmin = zmin) if bar: cbar = plt.colorbar(); cbar.ax.set_ylabel(cbartitle) plt.absolute_import try: vertices = [(vertex.X(), vertex.Y()) for vertex in pyliburo.py3dmodel.fetch.vertex_list_2_point_list(pyliburo.py3dmodel.fetch.topos_frm_compound(model)["vertex"])] shape = patches.PathPatch(Path(vertices), facecolor='white', lw=0) plt.gca().add_patch(shape) except TypeError: pass plt.show() try: fig.savefig(filename) except TypeError: return fig # def plot_along_line(self,X,Y, tick_list): # V = self.data.v # plt.plot(heights, SVFs_can, label='Canyon')
def UpdateData(self, products, micapsfile): self.UpdateExtents(products) extents = products.picture.extents xmax = extents.xmax xmin = extents.xmin ymax = extents.ymax ymin = extents.ymin path = products.map.clipborders[0].path if path is not None: self.AddPoints(self.x, self.y, self.z, path) # self.CreateArray() self.X = np.linspace(xmin, xmax, micapsfile.contour.grid[0]) self.Y = np.linspace(ymin, ymax, micapsfile.contour.grid[1]) # x = self.data['lon'] # y = self.data['lat'] # z = self.data['zvalue'] self.Z = griddata(self.x, self.y, self.z, self.X, self.Y, 'nn') self.X, self.Y = np.meshgrid(self.X, self.Y) self.min = min(self.z) self.max = max(self.z) self.distance = micapsfile.contour.step self.min = math.floor(self.min / self.distance) * self.distance self.max = math.ceil(self.max / self.distance) * self.distance # ??????legend?????????? ????????? self.UpdatePinLegendValue(micapsfile)
def plotRibbons(dta, saveFigName, index): """ creates ribbon-plot one-ribbon-at-a-time """ fig=gcf() ax=fig.gca(projection='3d') width=5 # assumes two indicies aren't too close together... y_min=0 # assumes given index (season len) is between 0-100 y_max=100 y=dta x=sorted(list(range(1,len(y)+1))*2) a=[index,index+width]*len(y) b=list(itertools.chain(*zip(y,y))) xi=np.linspace(min(x),max(x)) yi=np.linspace(min(a),max(a)) X,Y=np.meshgrid(xi,yi) Z=griddata(x,a,b,xi,yi, interp='linear') # to plot w/ y-axis colormapped: # colors =plt.cm.spectral( (Y-Y.min())/float((Y-Y.min()).max()) ) colors =plt.cm.spectral( (Y-y_min)/float(y_max-y_min) ) ax.plot_surface(X,Y,Z ,facecolors=colors, linewidth=0, shade=False ) # to plot w/ z-axis colormapped: # ax.plot_surface(X,Y,Z,rstride=50,cstride=1,cmap='Spectral') ax.set_zlim3d(np.min(Z),np.max(Z)) ax.grid(False) ax.w_xaxis.pane.set_visible(False) ax.w_yaxis.pane.set_visible(False) ax.w_zaxis.pane.set_color('gainsboro') # ax.set_title('Molecular spectra') # ax.set_xlim3d(0,23) # ax.set_xticks([1.6735,6.8367,12.0000,17.1633,22.3265]) # ax.set_xticklabels(['350','400','450','500','550']) # ax.set_xlabel('Wavelength (nm)') # ax.set_yticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5]) # ax.set_yticklabels(['1','2','3','4','5','6','7','8']) # ax.set_ylabel('Spectrum') # ax.set_zlim3d(0,2) # ax.set_zlabel('Absorbance') plt.savefig(str(saveFigName))
def plot_map(self): """ This function ... :return: """ # Inform the user log.info("Plotting a map of the heating fraction of the unevolved stellar population for a face-on view of the galaxy ...") # Determine the path to the plot file path = fs.join(self.analysis_heating_path, "map.pdf") plt.figure() x = np.ma.MaskedArray(self.absorptions["X coordinate of cell center"], mask=self.mask).compressed() y = np.ma.MaskedArray(self.absorptions["Y coordinate of cell center"], mask=self.mask).compressed() z = self.heating_fractions_compressed #plt.pcolormesh(x, y, z, cmap='RdBu', vmin=0.0, vmax=1.0) from matplotlib import mlab x_ticks = x y_ticks = y z_grid = mlab.griddata(x, y, z, x, y) from matplotlib.backends import backend_agg as agg from matplotlib import cm # plot # fig = Figure() # create the figure fig = plt.figure() agg.FigureCanvasAgg(fig) # attach the rasterizer ax = fig.add_subplot(1, 1, 1) # make axes to plot on ax.set_title("Interpolated Contour Plot of Experimental Data") ax.set_xlabel("X") ax.set_ylabel("Y") cmap = cm.get_cmap("hot") # get the "hot" color map contourset = ax.contourf(x_ticks, y_ticks, z_grid, 10, cmap=cmap) plt.savefig(path) plt.close() # -----------------------------------------------------------------
def adam_plot(self, func, obs, hist): hist['act'] = np.array(hist['act']).T hist['f'] = np.array(hist['f']).T hist['g'] = np.array(hist['g']).T if self.dimA == 1: xs = np.linspace(-1.+1e-8, 1.-1e-8, 100) ys = [func(obs[[0],:], [[xi]])[0] for xi in xs] fig = plt.figure() plt.plot(xs, ys) plt.plot(hist['act'][0,0,:], hist['f'][0,:], label='Adam') plt.legend() fname = os.path.join(FLAGS.outdir, 'adamPlt.png') print("Saving Adam plot to {}".format(fname)) plt.savefig(fname) plt.close(fig) elif self.dimA == 2: assert(False) else: xs = npr.uniform(-1., 1., (5000, self.dimA)) ys = np.array([func(obs[[0],:], [xi])[0] for xi in xs]) epi = np.hstack((xs, ys)) pca = PCA(n_components=2).fit(epi) W = pca.components_[:,:-1] xs_proj = xs.dot(W.T) fig = plt.figure() X = Y = np.linspace(xs_proj.min(), xs_proj.max(), 100) Z = griddata(xs_proj[:,0], xs_proj[:,1], ys.ravel(), X, Y, interp='linear') plt.contourf(X, Y, Z, 15) plt.colorbar() adam_x = hist['act'][:,0,:].T adam_x = adam_x.dot(W.T) plt.plot(adam_x[:,0], adam_x[:,1], label='Adam', color='k') plt.legend() fname = os.path.join(FLAGS.outdir, 'adamPlt.png') print("Saving Adam plot to {}".format(fname)) plt.savefig(fname) plt.close(fig)
def grid(x, y, z, resX=100, resY=100): """"Convert 3 column data to matplotlib grid Credit: Elyase of Stackoverflow.""" xi = np.linspace(min(x), max(x), resX) yi = np.linspace(min(y), max(y), resY) Z = griddata(x, y, z, xi, yi,interp='linear') X, Y = np.meshgrid(xi, yi) return X, Y, Z
