Python matplotlib.pyplot.hold() Examples
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code examples of matplotlib.pyplot.hold().
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Example #1
Source File: fig_comparison.py From ConvNetQuake with MIT License | 6 votes |
def fig_memory_usage(): # FAST memory x = [1,3,7,14,30,90,180] y_fast = [0.653,1.44,2.94,4.97,9.05,19.9,35.2] # ConvNetQuake y_convnet = [6.8*1e-5]*7 # Create figure plt.loglog(x,y_fast,"o-") plt.hold('on') plt.loglog(x,y_convnet,"o-") # plot markers plt.loglog(x,[1e-5,1e-5,1e-5,1e-5,1e-5,1e-5,1e-5],'o') plt.ylabel("Memory usage (GB)") plt.xlabel("Continous data duration (days)") plt.xlim(1,180) plt.grid("on") plt.savefig("./figures/memoryusage.eps") plt.close()
Example #2
Source File: common.py From rec-sys-experiments with Apache License 2.0 | 6 votes |
def plot_histogram(flname, title, x_label, datasets): """ Histogram dataset - list tuples of (frequencies, bins, style) """ plt.clf() plt.hold(True) max_bin = 0 for frequencies, bins, style in datasets: xs = [] ys = [] max_bin = max(max_bin, max(bins)) for i, f in enumerate(frequencies): xs.append(bins[i]) xs.append(bins[i+1]) ys.append(f) ys.append(f) plt.plot(xs, ys, style) plt.xlabel(x_label, fontsize=16) plt.ylabel("Occurrences", fontsize=16) plt.xlim([0, max_bin + 1]) plt.title(title, fontsize=18) plt.savefig(flname, DPI=200)
Example #3
Source File: common.py From rec-sys-experiments with Apache License 2.0 | 6 votes |
def plot_correlation(flname, title, x_label, y_label, dataset): """ Scatter plot with line of best fit dataset - tuple of (x_values, y_values) """ plt.clf() plt.hold(True) plt.scatter(dataset[0], dataset[1], alpha=0.7, color="k") xs = np.array(dataset[0]) ys = np.array(dataset[1]) A = np.vstack([xs, np.ones(len(xs))]).T m, c = np.linalg.lstsq(A, ys)[0] plt.plot(xs, m*xs + c, "c-") plt.xlabel(x_label, fontsize=16) plt.ylabel(y_label, fontsize=16) plt.xlim([0.25, max(dataset[0])]) plt.ylim([10., max(dataset[1])]) plt.title(title, fontsize=18) plt.savefig(flname, DPI=200)
Example #4
Source File: MinutiaeNet_utils.py From MinutiaeNet with MIT License | 6 votes |
def draw_minutiae(image, minutiae, fname, saveimage= False, r=15, drawScore=False): image = np.squeeze(image) fig = plt.figure() plt.imshow(image,cmap='gray') plt.hold(True) # Check if no minutiae if minutiae.shape[0] > 0: plt.plot(minutiae[:, 0], minutiae[:, 1], 'rs', fillstyle='none', linewidth=1) for x, y, o, s in minutiae: plt.plot([x, x+r*np.cos(o)], [y, y+r*np.sin(o)], 'r-') if drawScore == True: plt.text(x - 10, y - 10, '%.2f' % s, color='yellow', fontsize=4) plt.axis([0,image.shape[1],image.shape[0],0]) plt.axis('off') if saveimage: plt.savefig(fname, dpi=500, bbox_inches='tight', pad_inches = 0) plt.close(fig) else: plt.show() return
Example #5
Source File: plot_learn_curve2.py From PReMVOS with MIT License | 6 votes |
def doit(fn, col1, col2, tag): train = [] val = [] with open(fn) as f: for l in f: if "finished" in l: sp = l.split() #clip to 5 # tr = min(float(sp[col1]), 5.0) # va = min(float(sp[col2]), 5.0) tr = float(sp[col1]) va = float(sp[col2]) # if tr>1: tr = 1/tr # if va>1: va = 1/va train.append(tr) val.append(va) plt.plot(train, label="train") plt.hold(True) plt.plot(val, label="val") plt.legend() plt.title(fn + " " + tag) #plt.show()
Example #6
Source File: kerasExperiments.py From emailinsight with MIT License | 6 votes |
def make_plots(xs,ys,labels,title=None,x_name=None,y_name=None,y_bounds=None,save_to=None): colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k') handles = [] plt.figure() plt.hold(True) for i in range(len(labels)): plot, = make_plot(xs[i],ys[i],color=colors[i%len(colors)],new_fig=False) handles.append(plot) plt.legend(handles,labels) if title is not None: plt.title(title) if x_name is not None: plt.xlabel(x_name) if y_name is not None: plt.ylabel(y_name) if y_bounds is not None: plt.ylim(y_bounds) if save_to is not None: plt.savefig(save_to,bbox_inches='tight') plt.hold(False)
Example #7
Source File: terrain.py From director with BSD 3-Clause "New" or "Revised" License | 6 votes |
def drawSamples(self, nsamples): import matplotlib.pyplot as plt plt.figure(1) plt.clf() plt.hold(True) k = ConvexHull(self.bot_pts.T).vertices k = np.hstack((k, k[0])) n = self.planar_polyhedron.generators.shape[0] plt.plot(self.planar_polyhedron.generators.T[0,list(range(n)) + [0]], self.planar_polyhedron.generators.T[1,list(range(n)) + [0]], 'r.-') samples = sample_convex_polytope(self.c_space_polyhedron.A, self.c_space_polyhedron.b, 500) for i in range(samples.shape[1]): R = np.array([[np.cos(samples[2,i]), -np.sin(samples[2,i])], [np.sin(samples[2,i]), np.cos(samples[2,i])]]) V = R.dot(self.bot_pts[:,k]) V = V + samples[:2, i].reshape((2,1)) plt.plot(V[0,:], V[1,:], 'k-') plt.show()
Example #8
Source File: fig_comparison.py From ConvNetQuake with MIT License | 6 votes |
def fig_run_time(): # fast run time x_fast = [1,3,7,14,30,90,180] y_fast = [289,1.13*1e3,2.48*1e3,5.41*1e3,1.56*1e4, 6.61*1e4,1.98*1e5] x_auto = [1,3] y_auto = [1.54*1e4, 8.06*1e5] x_convnet = [1,3,7,14,30] y_convnet = [9,27,61,144,291] # create figure plt.loglog(x_auto,y_auto,"o-") plt.hold('on') plt.loglog(x_fast[0:5],y_fast[0:5],"o-") plt.loglog(x_convnet,y_convnet,"o-") # plot x markers plt.loglog(x_convnet,[1e0]*len(x_convnet),'o') # plot y markers y_markers = [1,60,3600,3600*24] plt.plot([1]*4,y_markers,'ko') plt.ylabel("run time (s)") plt.xlabel("continous data duration (days)") plt.xlim(1,35) plt.grid("on") plt.savefig("./figures/runtimes.eps")
Example #9
Source File: synthgen.py From SynthText with Apache License 2.0 | 6 votes |
def viz_textbb(fignum,text_im, bb_list,alpha=1.0): """ text_im : image containing text bb_list : list of 2x4xn_i boundinb-box matrices """ plt.close(fignum) plt.figure(fignum) plt.imshow(text_im) plt.hold(True) H,W = text_im.shape[:2] for i in xrange(len(bb_list)): bbs = bb_list[i] ni = bbs.shape[-1] for j in xrange(ni): bb = bbs[:,:,j] bb = np.c_[bb,bb[:,0]] plt.plot(bb[0,:], bb[1,:], 'r', linewidth=2, alpha=alpha) plt.gca().set_xlim([0,W-1]) plt.gca().set_ylim([H-1,0]) plt.show(block=False)
Example #10
Source File: sinesum1_movie.py From python_primer with MIT License | 6 votes |
def animate_series(fk, N, tmax, n, exact, exactname): t = np.linspace(0, tmax, n) s = np.zeros(len(t)) counter = 1 for k in range(0, N + 1): s = S(t, k, tmax) plt.plot(t, s, linewidth=2, color='#67001f') plt.hold(1) plt.plot(t, exact(t, tmax), '--', linewidth=2, color='#053061') plt.hold(0) plt.xlim(0, tmax) plt.ylim(-1.3, 1.3) plt.legend(['Sine sum - %d terms' % counter, exactname]) plt.savefig('tmp_%04d.png' % counter) counter += 1
Example #11
Source File: planet_orbit.py From python_primer with MIT License | 6 votes |
def animate_orbit(a, b, omega, n): tlist = np.linspace(0, 2 * np.pi / omega, n) xorbit, yorbit = orbit_path(tlist, a, b, omega) counter = 0 for t in tlist: x, y = orbit_path(t, a, b, omega) plt.plot(xorbit, yorbit, '--', color='#67001f', linewidth=2) plt.hold(1) plt.plot(x, y, 'ro', markerfacecolor='#2166ac', markeredgecolor='#053061', markeredgewidth=2, markersize=20) plt.hold(0) plt.xlim([xorbit.min() * 1.1, xorbit.max() * 1.1]) plt.ylim([yorbit.min() * 1.1, yorbit.max() * 1.1]) plt.xlabel('x') plt.ylabel('y') plt.title('Instantaneous velocity = %4f' % inst_vel(t, a, b, omega)) plt.savefig('tmp_%03d.png' % counter) counter += 1
Example #12
Source File: Utilities2D.py From laplacian-meshes with GNU General Public License v3.0 | 6 votes |
def getBarycentricCoords(A, B, C, X, checkValidity = True): T = np.array( [ [A.x - C.x, B.x - C.x ], [A.y - C.y, B.y - C.y] ] ) y = np.array( [ [X.x - C.x], [X.y - C.y] ] ) lambdas = linalg.solve(T, y) lambdas = lambdas.flatten() lambdas = np.append(lambdas, 1 - (lambdas[0] + lambdas[1])) if checkValidity: if (lambdas[0] < 0 or lambdas[1] < 0 or lambdas[2] < 0): print "ERROR: Not a convex combination; lambda = %s"%lambdas print "pointInsideConvexPolygon2D = %s"%pointInsideConvexPolygon2D([A, B, C], X, 0) plt.plot([A.x, B.x, C.x, A.x], [A.y, B.y, C.y, A.y], 'r') plt.hold(True) plt.plot([X.x], [X.y], 'b.') plt.show() assert (lambdas[0] >= 0 and lambdas[1] >= 0 and lambdas[2] >= 0) else: lambdas[0] = max(lambdas[0], 0) lambdas[1] = max(lambdas[1], 0) lambdas[2] = max(lambdas[2], 0) return lambdas
Example #13
Source File: animate_Taylor_series.py From python_primer with MIT License | 6 votes |
def animate_series(fk, M, N, xmin, xmax, ymin, ymax, n, exact, exactname): x = np.linspace(xmin, xmax, n) s = np.zeros(len(x)) counter = 1 for k in range(M, N + 1): s += fk(x, k) plt.plot(x, s, linewidth=2, color='#67001f') plt.hold(1) plt.plot(x, exact(x), '--', linewidth=2, color='#053061') plt.hold(0) plt.xlim(xmin, xmax) plt.ylim(ymin, ymax) plt.legend(['Taylor series approximation - %d terms' % counter, exactname]) plt.savefig('tmp_%04d.png' % counter) counter += 1
Example #14
Source File: plotting.py From geoio with MIT License | 6 votes |
def hist(data): """Convenience method to do all the plotting gymnastics to get a resonable looking histogram plot. Input: data - numpy array in gdal format - (bands, x, y) Returns: matplotlib figure handle Adapted from: http://nbviewer.jupyter.org/github/HyperionAnalytics/PyDataNYC2014/blob/master/color_image_processing.ipynb """ fig = plt.figure() ax1 = fig.add_subplot(111) plt.hold(True) for x in xrange(len(data[:,0,0])): counts, edges = np.histogram(data[x,:,:],bins=100) centers = [(edges[i]+edges[i+1])/2.0 for i,v in enumerate(edges[:-1])] ax1.plot(centers,counts) plt.hold(False) plt.show(block=False) # return fig
Example #15
Source File: lms.py From parametric_modeling with BSD 3-Clause "New" or "Revised" License | 6 votes |
def plot_traj(w, w_true, fname=None): """ Plot the trajectory of the filter coefficients Inputs: w: matrix of filter weights versus time w_true: vector of true filter weights fname: what filename to export the figure to. If None, then doesn't export """ plt.figure() n = np.arange(w.shape[0]) n_ones = np.ones(w.shape[0]) plt.hold(True) # NOTE: This construction places a limit of 4 on the filter order plt_colors = ['b', 'r', 'g', 'k'] for p in xrange(w.shape[1]): plt.plot(n, w[:,p], '{}-'.format(plt_colors[p]), label='w({})'.format(p)) plt.plot(n, w_true[p] * n_ones, '{}--'.format(plt_colors[p])) plt.xlabel('Iteration') plt.ylabel('Coefficients') plt.legend() if fname: plt.savefig(fname) plt.close()
Example #16
Source File: display.py From radiometric_normalization with Apache License 2.0 | 5 votes |
def plot_histograms(file_name, candidate_data_multiple_bands, reference_data_multiple_bands=None, # Default is for Blue-Green-Red-NIR: colour_order=['b', 'g', 'r', 'y'], x_limits=None, y_limits=None): logging.info('Display: Creating histogram plot - {}'.format(file_name)) fig = plt.figure() plt.hold(True) for colour, c_band in zip(colour_order, candidate_data_multiple_bands): c_bh, c_bins = numpy.histogram(c_band, bins=256) plt.plot(c_bins[:-1], c_bh, color=colour, linestyle='-', linewidth=2) if reference_data_multiple_bands: for colour, r_band in zip(colour_order, reference_data_multiple_bands): r_bh, r_bins = numpy.histogram(r_band, bins=256) plt.plot( r_bins[:-1], r_bh, color=colour, linestyle='--', linewidth=2) plt.xlabel('DN') plt.ylabel('Number of pixels') if x_limits: plt.xlim(x_limits) if y_limits: plt.ylim(y_limits) fig.savefig(file_name, bbox_inches='tight') plt.close(fig)
Example #17
Source File: MinutiaeNet_utils.py From MinutiaeNet with MIT License | 5 votes |
def draw_ori_on_img(img, ori, mask, fname, saveimage=False, coh=None, stride=16): ori = np.squeeze(ori) #mask = np.squeeze(np.round(mask)) img = np.squeeze(img) ori = ndimage.zoom(ori, np.array(img.shape)/np.array(ori.shape, dtype=float), order=0) if mask.shape != img.shape: mask = ndimage.zoom(mask, np.array(img.shape)/np.array(mask.shape, dtype=float), order=0) if coh is None: coh = np.ones_like(img) fig = plt.figure() plt.imshow(img,cmap='gray') plt.hold(True) for i in xrange(stride,img.shape[0],stride): for j in xrange(stride,img.shape[1],stride): if mask[i, j] == 0: continue x, y, o, r = j, i, ori[i,j], coh[i,j]*(stride*0.9) plt.plot([x, x+r*np.cos(o)], [y, y+r*np.sin(o)], 'r-') plt.axis([0,img.shape[1],img.shape[0],0]) plt.axis('off') if saveimage: plt.savefig(fname, bbox_inches='tight') plt.close(fig) else: plt.show() return
Example #18
Source File: MinutiaeNet_utils.py From MinutiaeNet with MIT License | 5 votes |
def draw_minutiae_overlay_with_score(image, minutiae, mnt_gt, fname, saveimage=False, r=15): image = np.squeeze(image) fig = plt.figure() plt.imshow(image, cmap='gray') plt.hold(True) if mnt_gt.shape[0] > 0: plt.plot(mnt_gt[:, 0], mnt_gt[:, 1], 'bs', fillstyle='none', linewidth=1) if mnt_gt.shape[1] > 3: for x, y, o, s in mnt_gt: plt.plot([x, x + r * np.cos(o)], [y, y + r * np.sin(o)], 'b-') plt.text(x - 10, y - 5, '%.2f' % s, color='green', fontsize=4) else: for x, y, o in mnt_gt: plt.plot([x, x + r * np.cos(o)], [y, y + r * np.sin(o)], 'b-') if minutiae.shape[0] > 0: plt.plot(minutiae[:, 0], minutiae[:, 1], 'rs', fillstyle='none', linewidth=1) for x, y, o, s in minutiae: plt.plot([x, x + r * np.cos(o)], [y, y + r * np.sin(o)], 'r-') plt.text(x-10,y-10,'%.2f'%s,color='yellow',fontsize=4) plt.axis([0, image.shape[1], image.shape[0], 0]) plt.axis('off') if saveimage: plt.savefig(fname, dpi=500, bbox_inches='tight') plt.close(fig) else: plt.show() return
Example #19
Source File: MinutiaeNet_utils.py From MinutiaeNet with MIT License | 5 votes |
def draw_minutiae_overlay(image, minutiae, mnt_gt, fname, saveimage= False, r=15, drawScore=False): image = np.squeeze(image) fig = plt.figure() plt.imshow(image,cmap='gray') plt.hold(True) if mnt_gt.shape[1] > 3: mnt_gt = mnt_gt[:,:3] if mnt_gt.shape[0] > 0: if mnt_gt.shape[1] > 3: mnt_gt = mnt_gt[:, :3] plt.plot(mnt_gt[:, 0], mnt_gt[:, 1], 'bs', fillstyle='none', linewidth=1) for x, y, o in mnt_gt: plt.plot([x, x+r*np.cos(o)], [y, y+r*np.sin(o)], 'b-') if minutiae.shape[0] > 0: plt.plot(minutiae[:, 0], minutiae[:, 1], 'rs', fillstyle='none', linewidth=1) for x, y, o in minutiae: plt.plot([x, x+r*np.cos(o)], [y, y+r*np.sin(o)], 'r-') if drawScore == True: plt.text(x - 10, y - 10, '%.2f' % s, color='yellow', fontsize=4) plt.axis([0,image.shape[1],image.shape[0],0]) plt.axis('off') plt.show() if saveimage: plt.savefig(fname, dpi=500, bbox_inches='tight') plt.close(fig) else: plt.show() return
Example #20
Source File: plots.py From PCWG with MIT License | 5 votes |
def plotPowerCurveSensitivity(self, sensCol): try: df = self.analysis.powerCurveSensitivityResults[sensCol].reset_index() from matplotlib import pyplot as plt plt.ioff() fig = plt.figure(figsize = (12,5)) fig.suptitle('Power Curve Sensitivity to %s' % sensCol) ax1 = fig.add_subplot(121) ax1.hold(True) ax2 = fig.add_subplot(122) ax2.hold(True) power_column = self.analysis.measuredTurbulencePower if self.analysis.turbRenormActive else self.analysis.actualPower for label in self.analysis.sensitivityLabels.keys(): filt = df['Bin'] == label ax1.plot(df['Wind Speed Bin'][filt], df[power_column][filt], label = label, color = self.analysis.sensitivityLabels[label]) ax2.plot(df['Wind Speed Bin'][filt], df['Energy Delta MWh'][filt], label = label, color = self.analysis.sensitivityLabels[label]) ax1.set_xlabel('Wind Speed (m/s)') ax1.set_ylabel('Power (kW)') ax2.set_xlabel('Wind Speed (m/s)') ax2.set_ylabel('Energy Difference from Mean (MWh)') box1 = ax1.get_position() box2 = ax2.get_position() ax1.set_position([box1.x0 - 0.05 * box1.width, box1.y0 + box1.height * 0.17, box1.width * 0.95, box1.height * 0.8]) ax2.set_position([box2.x0 + 0.05 * box2.width, box2.y0 + box2.height * 0.17, box2.width * 1.05, box2.height * 0.8]) handles, labels = ax1.get_legend_handles_labels() fig.legend(handles, labels, loc='lower center', ncol = len(self.analysis.sensitivityLabels.keys()), fancybox = True, shadow = True) file_out = self.path + os.sep + 'Power Curve Sensitivity to %s.png' % sensCol chckMake(self.path) fig.savefig(file_out) plt.close() except: Status.add("Tried to make a plot of power curve sensitivity to %s. Couldn't." % sensCol, verbosity=2)
Example #21
Source File: ABuTradeDrawer.py From abu with GNU General Public License v3.0 | 5 votes |
def plot_kp_xd(kp_summary, kl_pd_xd_mean, title=None): """根据有bk_summary属性的kp交易因子进行可视化,暂时未迁移完成""" plt.figure() plt.plot(list(range(0, len(kl_pd_xd_mean))), kl_pd_xd_mean['close']) for kp in kp_summary.kp_xd_obj_list: plt.hold(True) plt.plot(kp.break_index, kl_pd_xd_mean['close'][kp.break_index], 'ro', markersize=8, markeredgewidth=1.5, markerfacecolor='None', markeredgecolor='r') if title is not None: plt.title(title) plt.grid(True)
Example #22
Source File: ABuTradeDrawer.py From abu with GNU General Public License v3.0 | 5 votes |
def plot_bk_xd(bk_summary, kl_pd_xd_mean, title=None): """根据有bk_summary属性的bk交易因子进行可视化,暂时未迁移完成""" plt.figure() plt.plot(list(range(0, len(kl_pd_xd_mean))), kl_pd_xd_mean['close']) for bk in bk_summary.bk_xd_obj_list: plt.hold(True) pc = 'r' if bk.break_sucess is True else 'g' plt.plot(bk.break_index, kl_pd_xd_mean['close'][bk.break_index], 'ro', markersize=12, markeredgewidth=1.5, markerfacecolor='None', markeredgecolor=pc) if title is not None: plt.title(title) plt.grid(True)
Example #23
Source File: gridder_obj.py From geoist with MIT License | 5 votes |
def map2DGrid(ax, grid, tstr, xlen=1.0, ylen=1.0, isLeft=False): """ grid is a Grid2D object """ xmin,xmax,ymin,ymax = grid.getBounds() pdata = grid.getData() nr,nc = pdata.shape lonrange = np.linspace(xmin,xmax,num=nc) latrange = np.linspace(ymin,ymax,num=nr) lon,lat = np.meshgrid(lonrange,latrange) latmean = np.mean([ymin,ymax]) lonmean = np.mean([xmin,xmax]) m = Basemap(llcrnrlon=xmin,llcrnrlat=ymin,urcrnrlon=xmax,urcrnrlat=ymax,\ rsphere=(6378137.00,6356752.3142),\ resolution='c',area_thresh=1000.,projection='lcc',\ lat_1=latmean,lon_0=lonmean,ax=ax) # draw coastlines and political boundaries. m.drawcoastlines() #m.drawcountries() #m.drawstates() lons = np.arange(xmin,xmax,xlen) lats = np.arange(ymin,ymax,ylen) if isLeft: labels = labels=[1,0,0,0] else: labels = labels=[0,0,0,0] m.drawparallels(lats,labels=labels,color='white',fmt='%.1f') # draw parallels m.drawmeridians(lons,labels=[0,0,0,1],color='white',fmt='%.1f') # draw meridians pmesh = m.pcolormesh(lon,lat,np.flipud(grid.getData()),latlon=True) #plt.hold(True) ax.set_title(tstr) m.colorbar(pmesh)
Example #24
Source File: utils.py From SMIT with MIT License | 5 votes |
def plot_txt(txt_file): import matplotlib.pyplot as plt lines = [line.strip().split() for line in open(txt_file).readlines()] legends = {idx: line for idx, line in enumerate(lines[0][1:])} # 0 is epochs lines = lines[1:] epochs = [] losses = {loss: [] for loss in legends.values()} for line in lines: epochs.append(line[0]) for idx, loss in enumerate(line[1:]): losses[legends[idx]].append(float(loss)) import pylab as pyl plot_file = txt_file.replace('.txt', '.pdf') _min = 4 if len(losses.keys()) > 9 else 3 for idx, loss in enumerate(losses.keys()): # plot_file = txt_file.replace('.txt','_{}.jpg'.format(loss)) plt.rcParams.update({'font.size': 10}) ax1 = plt.subplot(3, _min, idx + 1) # err = plt.plot(epochs, losses[loss], 'r.-') err = plt.plot(epochs, losses[loss], 'b.-') plt.setp(err, linewidth=2.5) plt.ylabel(loss.capitalize(), fontsize=16) plt.xlabel('Epoch', fontsize=16) ax1.tick_params(labelsize=8) plt.hold(False) plt.grid() plt.subplots_adjust( left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.5) pyl.savefig(plot_file, dpi=100) # ==================================================================# # ==================================================================#
Example #25
Source File: visualize_results.py From SynthText with Apache License 2.0 | 5 votes |
def viz_textbb(text_im, charBB_list, wordBB, alpha=1.0): """ text_im : image containing text charBB_list : list of 2x4xn_i bounding-box matrices wordBB : 2x4xm matrix of word coordinates """ plt.close(1) plt.figure(1) plt.imshow(text_im) plt.hold(True) H,W = text_im.shape[:2] # plot the character-BB: for i in xrange(len(charBB_list)): bbs = charBB_list[i] ni = bbs.shape[-1] for j in xrange(ni): bb = bbs[:,:,j] bb = np.c_[bb,bb[:,0]] plt.plot(bb[0,:], bb[1,:], 'r', alpha=alpha/2) # plot the word-BB: for i in xrange(wordBB.shape[-1]): bb = wordBB[:,:,i] bb = np.c_[bb,bb[:,0]] plt.plot(bb[0,:], bb[1,:], 'g', alpha=alpha) # visualize the indiv vertices: vcol = ['r','g','b','k'] for j in xrange(4): plt.scatter(bb[0,j],bb[1,j],color=vcol[j]) plt.gca().set_xlim([0,W-1]) plt.gca().set_ylim([H-1,0]) plt.show(block=False)
Example #26
Source File: ShortTermFeatures.py From pyAudioAnalysis with Apache License 2.0 | 5 votes |
def chroma_features(signal, sampling_rate, num_fft): # TODO: 1 complexity # TODO: 2 bug with large windows num_chroma, num_freqs_per_chroma = \ chroma_features_init(num_fft, sampling_rate) chroma_names = ['A', 'A#', 'B', 'C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#'] spec = signal ** 2 if num_chroma.max() < num_chroma.shape[0]: C = np.zeros((num_chroma.shape[0],)) C[num_chroma] = spec C /= num_freqs_per_chroma[num_chroma] else: I = np.nonzero(num_chroma > num_chroma.shape[0])[0][0] C = np.zeros((num_chroma.shape[0],)) C[num_chroma[0:I - 1]] = spec C /= num_freqs_per_chroma final_matrix = np.zeros((12, 1)) newD = int(np.ceil(C.shape[0] / 12.0) * 12) C2 = np.zeros((newD,)) C2[0:C.shape[0]] = C C2 = C2.reshape(int(C2.shape[0] / 12), 12) # for i in range(12): # finalC[i] = np.sum(C[i:C.shape[0]:12]) final_matrix = np.matrix(np.sum(C2, axis=0)).T final_matrix /= spec.sum() # ax = plt.gca() # plt.hold(False) # plt.plot(finalC) # ax.set_xticks(range(len(chromaNames))) # ax.set_xticklabels(chromaNames) # xaxis = np.arange(0, 0.02, 0.01); # ax.set_yticks(range(len(xaxis))) # ax.set_yticklabels(xaxis) # plt.show(block=False) # plt.draw() return chroma_names, final_matrix
Example #27
Source File: utils.py From text_renderer with MIT License | 5 votes |
def viz_img(text_im, fignum=1): """ text_im : image containing text """ text_im = text_im.astype(int) plt.close(fignum) plt.figure(fignum) plt.imshow(text_im, cmap='gray') plt.show(block=True) # plt.hold(True) # # H, W = text_im.shape[:2] # plt.gca().set_xlim([0, W - 1]) # plt.gca().set_ylim([H - 1, 0]) # plt.show(block=True)
Example #28
Source File: sct_compute_hausdorff_distance.py From spinalcordtoolbox with MIT License | 5 votes |
def show_results(self): import seaborn as sns import matplotlib.pyplot as plt import pandas as pd plt.hold(True) sns.set(style="whitegrid", palette="pastel", color_codes=True) plt.figure(figsize=(35, 20)) data_dist = {"distances": [], "image": [], "slice": []} if self.dim_im == 2: data_dist["distances"].append([dist * self.dim_pix for dist in self.dist1_distribution]) data_dist["image"].append(len(self.dist1_distribution) * [1]) data_dist["slice"].append(len(self.dist1_distribution) * [0]) data_dist["distances"].append([dist * self.dim_pix for dist in self.dist2_distribution]) data_dist["image"].append(len(self.dist2_distribution) * [2]) data_dist["slice"].append(len(self.dist2_distribution) * [0]) if self.dim_im == 3: for i in range(len(self.distances)): data_dist["distances"].append([dist * self.dim_pix for dist in self.dist1_distribution[i]]) data_dist["image"].append(len(self.dist1_distribution[i]) * [1]) data_dist["slice"].append(len(self.dist1_distribution[i]) * [i]) data_dist["distances"].append([dist * self.dim_pix for dist in self.dist2_distribution[i]]) data_dist["image"].append(len(self.dist2_distribution[i]) * [2]) data_dist["slice"].append(len(self.dist2_distribution[i]) * [i]) for k in data_dist.keys(): # flatten the lists in data_dist data_dist[k] = [item for sublist in data_dist[k] for item in sublist] data_dist = pd.DataFrame(data_dist) sns.violinplot(x="slice", y="distances", hue="image", data=data_dist, split=True, inner="point", cut=0) plt.savefig('violin_plot.png') # plt.show() # ----------------------------------------------------------------------------------------------------------------------
Example #29
Source File: testokid.py From modred with BSD 2-Clause "Simplified" License | 4 votes |
def test_OKID(self): rtol = 1e-8 atol = 1e-10 for case in ['SISO', 'SIMO', 'MISO', 'MIMO']: inputs = util.load_array_text( join(join(self.test_dir, case), 'inputs.txt')) outputs = util.load_array_text( join(join(self.test_dir, case), 'outputs.txt')) (num_inputs, nt) = inputs.shape (num_outputs, nt2) = outputs.shape assert(nt2 == nt) Markovs_true = np.zeros((nt, num_outputs, num_inputs)) tmp = util.load_array_text( join(join(self.test_dir, case), 'Markovs_Matlab_output1.txt')) tmp = tmp.reshape((num_inputs, -1)) num_Markovs_OKID = tmp.shape[1] Markovs_Matlab = np.zeros( (num_Markovs_OKID, num_outputs, num_inputs)) for i_out in range(num_outputs): data = util.load_array_text( join(join( self.test_dir, case), 'Markovs_Matlab_output%d.txt' % (i_out + 1))) if num_inputs > 1: data = np.swapaxes(data, 0, 1) Markovs_Matlab[:, i_out, :] = data data = util.load_array_text(join( join(self.test_dir, case), 'Markovs_true_output%d.txt' % (i_out + 1))) if num_inputs > 1: data = np.swapaxes(data, 0, 1) Markovs_true[:,i_out,:] = data Markovs_python = OKID(inputs, outputs, num_Markovs_OKID) if plot: plt.figure(figsize=(14,10)) for output_num in range(num_outputs): for input_num in range(num_inputs): plt.subplot(num_outputs, num_inputs, output_num*(num_inputs) + input_num + 1) plt.hold(True) plt.plot(Markovs_true[:,output_num,input_num],'k*-') plt.plot(Markovs_Matlab[:,output_num,input_num],'b--') plt.plot(Markovs_python[:,output_num,input_num],'r.') plt.legend(['True', 'Matlab OKID', 'Python OKID']) plt.title('Input %d to output %d'%(input_num+1, output_num+1)) plt.show() np.testing.assert_allclose( Markovs_python.squeeze(), Markovs_Matlab.squeeze(), rtol=rtol, atol=atol) np.testing.assert_allclose( Markovs_python.squeeze(), Markovs_true[:num_Markovs_OKID].squeeze(), rtol=rtol, atol=atol)
Example #30
Source File: utils.py From TensorKart with MIT License | 4 votes |
def viewer(sample): image_files, joystick_values = load_sample(sample) plotData = [] plt.ion() plt.figure('viewer', figsize=(16, 6)) for i in range(len(image_files)): # joystick print(i, " ", joystick_values[i,:]) # format data plotData.append( joystick_values[i,:] ) if len(plotData) > 30: plotData.pop(0) x = np.asarray(plotData) # image (every 3rd) if (i % 3 == 0): plt.subplot(121) image_file = image_files[i] img = mpimg.imread(image_file) plt.imshow(img) # plot plt.subplot(122) plt.plot(range(i,i+len(plotData)), x[:,0], 'r') plt.hold(True) plt.plot(range(i,i+len(plotData)), x[:,1], 'b') plt.plot(range(i,i+len(plotData)), x[:,2], 'g') plt.plot(range(i,i+len(plotData)), x[:,3], 'k') plt.plot(range(i,i+len(plotData)), x[:,4], 'y') plt.draw() plt.hold(False) plt.pause(0.0001) # seconds i += 1 # prepare training data