Python matplotlib.pyplot.legend() Examples
The following are 30 code examples for showing how to use matplotlib.pyplot.legend(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: pruning_yolov3 Author: zbyuan File: utils.py License: GNU General Public License v3.0 | 7 votes |
def plot_wh_methods(): # from utils.utils import *; plot_wh_methods() # Compares the two methods for width-height anchor multiplication # https://github.com/ultralytics/yolov3/issues/168 x = np.arange(-4.0, 4.0, .1) ya = np.exp(x) yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 fig = plt.figure(figsize=(6, 3), dpi=150) plt.plot(x, ya, '.-', label='yolo method') plt.plot(x, yb ** 2, '.-', label='^2 power method') plt.plot(x, yb ** 2.5, '.-', label='^2.5 power method') plt.xlim(left=-4, right=4) plt.ylim(bottom=0, top=6) plt.xlabel('input') plt.ylabel('output') plt.legend() fig.tight_layout() fig.savefig('comparison.png', dpi=200)
Example 2
Project: MomentumContrast.pytorch Author: peisuke File: test.py License: MIT License | 6 votes |
def show(mnist, targets, ret): target_ids = range(len(set(targets))) colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k', 'violet', 'orange', 'purple'] plt.figure(figsize=(12, 10)) ax = plt.subplot(aspect='equal') for label in set(targets): idx = np.where(np.array(targets) == label)[0] plt.scatter(ret[idx, 0], ret[idx, 1], c=colors[label], label=label) for i in range(0, len(targets), 250): img = (mnist[i][0] * 0.3081 + 0.1307).numpy()[0] img = OffsetImage(img, cmap=plt.cm.gray_r, zoom=0.5) ax.add_artist(AnnotationBbox(img, ret[i])) plt.legend() plt.show()
Example 3
Project: Stock-Price-Prediction Author: dhingratul File: helper.py License: MIT License | 6 votes |
def plot_mul(Y_hat, Y, pred_len): """ PLots the predicted data versus true data Input: Predicted data, True Data, Length of prediction Output: return plot Note: Run from timeSeriesPredict.py """ fig = plt.figure(facecolor='white') ax = fig.add_subplot(111) ax.plot(Y, label='Y') # Print the predictions in its respective series-length for i, j in enumerate(Y_hat): shift = [None for p in range(i * pred_len)] plt.plot(shift + j, label='Y_hat') plt.legend() plt.show()
Example 4
Project: neural-fingerprinting Author: StephanZheng File: util.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def compute_roc(y_true, y_pred, plot=False): """ TODO :param y_true: ground truth :param y_pred: predictions :param plot: :return: """ fpr, tpr, _ = roc_curve(y_true, y_pred) auc_score = auc(fpr, tpr) if plot: plt.figure(figsize=(7, 6)) plt.plot(fpr, tpr, color='blue', label='ROC (AUC = %0.4f)' % auc_score) plt.legend(loc='lower right') plt.title("ROC Curve") plt.xlabel("FPR") plt.ylabel("TPR") plt.show() return fpr, tpr, auc_score
Example 5
Project: neural-fingerprinting Author: StephanZheng File: util.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def compute_roc_rfeinman(probs_neg, probs_pos, plot=False): """ TODO :param probs_neg: :param probs_pos: :param plot: :return: """ probs = np.concatenate((probs_neg, probs_pos)) labels = np.concatenate((np.zeros_like(probs_neg), np.ones_like(probs_pos))) fpr, tpr, _ = roc_curve(labels, probs) auc_score = auc(fpr, tpr) if plot: plt.figure(figsize=(7, 6)) plt.plot(fpr, tpr, color='blue', label='ROC (AUC = %0.4f)' % auc_score) plt.legend(loc='lower right') plt.title("ROC Curve") plt.xlabel("FPR") plt.ylabel("TPR") plt.show() return fpr, tpr, auc_score
Example 6
Project: deep-learning-note Author: wdxtub File: 6_bias_variance.py License: MIT License | 6 votes |
def plot_learning_curve(X, y, Xval, yval, l=0): training_cost, cv_cost = [], [] m = X.shape[0] for i in range(1, m + 1): # regularization applies here for fitting parameters res = linear_regression_np(X[:i, :], y[:i], l=l) # remember, when you compute the cost here, you are computing # non-regularized cost. Regularization is used to fit parameters only tc = cost(res.x, X[:i, :], y[:i]) cv = cost(res.x, Xval, yval) training_cost.append(tc) cv_cost.append(cv) plt.plot(np.arange(1, m + 1), training_cost, label='training cost') plt.plot(np.arange(1, m + 1), cv_cost, label='cv cost') plt.legend(loc=1)
Example 7
Project: deep-learning-note Author: wdxtub File: simulate_sin.py License: MIT License | 6 votes |
def run_eval(sess, test_X, test_y): ds = tf.data.Dataset.from_tensor_slices((test_X, test_y)) ds = ds.batch(1) X, y = ds.make_one_shot_iterator().get_next() with tf.variable_scope("model", reuse=True): prediction, _, _ = lstm_model(X, [0.0], False) predictions = [] labels = [] for i in range(TESTING_EXAMPLES): p, l = sess.run([prediction, y]) predictions.append(p) labels.append(l) predictions = np.array(predictions).squeeze() labels = np.array(labels).squeeze() rmse = np.sqrt(((predictions-labels) ** 2).mean(axis=0)) print("Mean Square Error is: %f" % rmse) plt.figure() plt.plot(predictions, label='predictions') plt.plot(labels, label='real_sin') plt.legend() plt.show()
Example 8
Project: fullrmc Author: bachiraoun File: plotFigures.py License: GNU Affero General Public License v3.0 | 6 votes |
def plot(PDF, figName, imgpath, show=False, save=True): # plot output = PDF.get_constraint_value() plt.plot(PDF.experimentalDistances,PDF.experimentalPDF, 'ro', label="experimental", markersize=7.5, markevery=1 ) plt.plot(PDF.shellsCenter, output["pdf"], 'k', linewidth=3.0, markevery=25, label="total" ) styleIndex = 0 for key in output: val = output[key] if key in ("pdf_total", "pdf"): continue elif "inter" in key: plt.plot(PDF.shellsCenter, val, STYLE[styleIndex], markevery=5, label=key.split('rdf_inter_')[1] ) styleIndex+=1 plt.legend(frameon=False, ncol=1) # set labels plt.title("$\\chi^{2}=%.6f$"%PDF.squaredDeviations, size=20) plt.xlabel("$r (\AA)$", size=20) plt.ylabel("$g(r)$", size=20) # show plot if save: plt.savefig(figName) if show: plt.show() plt.close()
Example 9
Project: keras-anomaly-detection Author: chen0040 File: plot_utils.py License: MIT License | 6 votes |
def visualize_anomaly(y_true, reconstruction_error, threshold): error_df = pd.DataFrame({'reconstruction_error': reconstruction_error, 'true_class': y_true}) print(error_df.describe()) groups = error_df.groupby('true_class') fig, ax = plt.subplots() for name, group in groups: ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='', label="Fraud" if name == 1 else "Normal") ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors="r", zorder=100, label='Threshold') ax.legend() plt.title("Reconstruction error for different classes") plt.ylabel("Reconstruction error") plt.xlabel("Data point index") plt.show()
Example 10
Project: pruning_yolov3 Author: zbyuan File: utils.py License: GNU General Public License v3.0 | 6 votes |
def plot_results(start=0, stop=0): # from utils.utils import *; plot_results() # Plot training results files 'results*.txt' fig, ax = plt.subplots(2, 5, figsize=(14, 7)) ax = ax.ravel() s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall', 'val GIoU', 'val Objectness', 'val Classification', 'mAP', 'F1'] for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T n = results.shape[1] # number of rows x = range(start, min(stop, n) if stop else n) for i in range(10): y = results[i, x] if i in [0, 1, 2, 5, 6, 7]: y[y == 0] = np.nan # dont show zero loss values ax[i].plot(x, y, marker='.', label=f.replace('.txt', '')) ax[i].set_title(s[i]) if i in [5, 6, 7]: # share train and val loss y axes ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) fig.tight_layout() ax[1].legend() fig.savefig('results.png', dpi=200)
Example 11
Project: pruning_yolov3 Author: zbyuan File: utils.py License: GNU General Public License v3.0 | 6 votes |
def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_results_overlay() # Plot training results files 'results*.txt', overlaying train and val losses s = ['train', 'train', 'train', 'Precision', 'mAP', 'val', 'val', 'val', 'Recall', 'F1'] # legends t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T n = results.shape[1] # number of rows x = range(start, min(stop, n) if stop else n) fig, ax = plt.subplots(1, 5, figsize=(14, 3.5)) ax = ax.ravel() for i in range(5): for j in [i, i + 5]: y = results[j, x] if i in [0, 1, 2]: y[y == 0] = np.nan # dont show zero loss values ax[i].plot(x, y, marker='.', label=s[j]) ax[i].set_title(t[i]) ax[i].legend() ax[i].set_ylabel(f) if i == 0 else None # add filename fig.tight_layout() fig.savefig(f.replace('.txt', '.png'), dpi=200)
Example 12
Project: cs294-112_hws Author: xuwd11 File: plot_part1.py License: MIT License | 6 votes |
def plot_12(data): r1, r2, r3, r4 = data plt.figure() add_plot(r1, 'MeanReward100Episodes'); add_plot(r1, 'BestMeanReward', 'vanilla DQN'); add_plot(r2, 'MeanReward100Episodes'); add_plot(r2, 'BestMeanReward', 'double DQN'); plt.xlabel('Time step'); plt.ylabel('Reward'); plt.legend(); plt.savefig( os.path.join('results', 'p12.png'), bbox_inches='tight', transparent=True, pad_inches=0.1 )
Example 13
Project: cs294-112_hws Author: xuwd11 File: plot_part1.py License: MIT License | 6 votes |
def plot_13(data): r1, r2, r3, r4 = data plt.figure() add_plot(r3, 'MeanReward100Episodes'); add_plot(r3, 'BestMeanReward', 'gamma = 0.9'); add_plot(r2, 'MeanReward100Episodes'); add_plot(r2, 'BestMeanReward', 'gamma = 0.99'); add_plot(r4, 'MeanReward100Episodes'); add_plot(r4, 'BestMeanReward', 'gamma = 0.999'); plt.legend(); plt.xlabel('Time step'); plt.ylabel('Reward'); plt.savefig( os.path.join('results', 'p13.png'), bbox_inches='tight', transparent=True, pad_inches=0.1 )
Example 14
Project: cs294-112_hws Author: xuwd11 File: plot_3.py License: MIT License | 6 votes |
def plot_3(data): x = data.Iteration.unique() y_mean = data.groupby('Iteration').mean() y_std = data.groupby('Iteration').std() sns.set(style="darkgrid", font_scale=1.5) value = 'AverageReturn' plt.plot(x, y_mean[value], label=data['Condition'].unique()[0] + '_train'); plt.fill_between(x, y_mean[value] - y_std[value], y_mean[value] + y_std[value], alpha=0.2); value = 'ValAverageReturn' plt.plot(x, y_mean[value], label=data['Condition'].unique()[0] + '_test'); plt.fill_between(x, y_mean[value] - y_std[value], y_mean[value] + y_std[value], alpha=0.2); plt.xlabel('Iteration') plt.ylabel('AverageReturn') plt.legend(loc='best')
Example 15
Project: Pytorch-Networks Author: HaiyangLiu1997 File: utils.py License: MIT License | 6 votes |
def plot_result_data(acc_total, acc_val_total, loss_total, losss_val_total, cfg_path, epoch): import matplotlib.pyplot as plt y = range(epoch) plt.plot(y,acc_total,linestyle="-", linewidth=1,label='acc_train') plt.plot(y,acc_val_total,linestyle="-", linewidth=1,label='acc_val') plt.legend(('acc_train', 'acc_val'), loc='upper right') plt.xlabel("Training Epoch") plt.ylabel("Acc on dataset") plt.savefig('{}/acc.png'.format(cfg_path)) plt.cla() plt.plot(y,loss_total,linestyle="-", linewidth=1,label='loss_train') plt.plot(y,losss_val_total,linestyle="-", linewidth=1,label='loss_val') plt.legend(('loss_train', 'loss_val'), loc='upper right') plt.xlabel("Training Epoch") plt.ylabel("Loss on dataset") plt.savefig('{}/loss.png'.format(cfg_path))
Example 16
Project: spinn Author: stanfordnlp File: analyze_log.py License: MIT License | 6 votes |
def ShowPlots(subplot=False): for log_ind, path in enumerate(FLAGS.path.split(":")): log = Log(path) if subplot: plt.subplot(len(FLAGS.path.split(":")), 1, log_ind + 1) for index in FLAGS.index.split(","): index = int(index) for attr in ["pred_acc", "parse_acc", "total_cost", "xent_cost", "l2_cost", "action_cost"]: if getattr(FLAGS, attr): if "cost" in attr: assert index == 0, "costs only associated with training log" steps, val = zip(*[(l.step, getattr(l, attr)) for l in log.corpus[index] if l.step < FLAGS.iters]) dct = {} for k, v in zip(steps, val): dct[k] = max(v, dct[k]) if k in dct else v steps, val = zip(*sorted(dct.iteritems())) plt.plot(steps, val, label="Log%d:%s-%d" % (log_ind, attr, index)) plt.xlabel("No. of training iteration") plt.ylabel(FLAGS.ylabel) if FLAGS.legend: plt.legend() plt.show()
Example 17
Project: Bidirectiona-LSTM-for-text-summarization- Author: DeepsMoseli File: lstm_Attention.py License: MIT License | 6 votes |
def plot_training(history): print(history.history.keys()) # "Accuracy" plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'validation'], loc='upper left') plt.show() # "Loss" plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'validation'], loc='upper left') plt.show()
Example 18
Project: pointnet-registration-framework Author: vinits5 File: plot_threshold_vs_success_trans.py License: MIT License | 6 votes |
def make_plot(files, labels): plt.figure() for file_idx in range(len(files)): rot_err, trans_err = read_csv(files[file_idx]) success_dict = count_success(trans_err) x_range = success_dict.keys() x_range.sort() success = [] for i in x_range: success.append(success_dict[i]) success = np.array(success)/total_cases plt.plot(x_range, success, linewidth=3, label=labels[file_idx]) # plt.scatter(x_range, success, s=50) plt.ylabel('Success Ratio', fontsize=40) plt.xlabel('Threshold for Translation Error', fontsize=40) plt.tick_params(labelsize=40, width=3, length=10) plt.grid(True) plt.ylim(0,1.005) plt.yticks(np.arange(0,1.2,0.2)) plt.xticks(np.arange(0,2.1,0.2)) plt.xlim(0,2) plt.legend(fontsize=30, loc=4)
Example 19
Project: pymoo Author: msu-coinlab File: plotting.py License: Apache License 2.0 | 6 votes |
def plot(*args, show=True, labels=None, no_fill=False, **kwargs): F = args[0] if F.ndim == 1: print("Cannot plot a one dimensional array.") return n_dim = F.shape[1] if n_dim == 2: ret = plot_2d(*args, labels=labels, no_fill=no_fill, **kwargs) elif n_dim == 3: ret = plot_3d(*args, labels=labels, no_fill=no_fill, **kwargs) else: print("Cannot plot a %s dimensional array." % n_dim) return if labels: plt.legend() if show: plt.show() return ret
Example 20
Project: pyscf Author: pyscf File: m_dos_pdos_eigenvalues.py License: Apache License 2.0 | 6 votes |
def dosplot (filename = None, data = None, fermi = None): if (filename is not None): data = np.loadtxt(filename) elif (data is not None): data = data import matplotlib.pyplot as plt from matplotlib import rc plt.rc('text', usetex=True) plt.rc('font', family='serif') plt.plot(data.T[0], data.T[1], label='MF Spin-UP', linestyle=':',color='r') plt.fill_between(data.T[0], 0, data.T[1], facecolor='r',alpha=0.1, interpolate=True) plt.plot(data.T[0], data.T[2], label='QP Spin-UP',color='r') plt.fill_between(data.T[0], 0, data.T[2], facecolor='r',alpha=0.5, interpolate=True) plt.plot(data.T[0],-data.T[3], label='MF Spin-DN', linestyle=':',color='b') plt.fill_between(data.T[0], 0, -data.T[3], facecolor='b',alpha=0.1, interpolate=True) plt.plot(data.T[0],-data.T[4], label='QP Spin-DN',color='b') plt.fill_between(data.T[0], 0, -data.T[4], facecolor='b',alpha=0.5, interpolate=True) if (fermi!=None): plt.axvline(x=fermi ,color='k', linestyle='--') #label='Fermi Energy' plt.axhline(y=0,color='k') plt.title('Total DOS', fontsize=20) plt.xlabel('Energy (eV)', fontsize=15) plt.ylabel('Density of States (electron/eV)', fontsize=15) plt.legend() plt.savefig("dos_eigen.svg", dpi=900) plt.show()
Example 21
Project: pedestrian-haar-based-detector Author: felipecorrea File: detect.py License: GNU General Public License v2.0 | 5 votes |
def generate_histogram(img): hist,bins = np.histogram(img.flatten(),256,[0,256]) #cumulative distribution function calculation cdf = hist.cumsum() plt.plot(cdf_normalized, color = 'b') plt.hist(img.flatten(),256,[0,256], color = 'r') plt.xlim([0,256]) plt.legend(('cdf','histogram'), loc = 'upper left') plt.show() return hist
Example 22
Project: pedestrian-haar-based-detector Author: felipecorrea File: histcomparison.py License: GNU General Public License v2.0 | 5 votes |
def generate_histogram(img): hist,bins = np.histogram(img.flatten(),256,[0,256]) #cumulative distribution function calculation cdf = hist.cumsum() cdf_normalized = cdf *hist.max()/ cdf.max() # this line not necessary. plt.plot(cdf_normalized, color = 'b') plt.hist(img.flatten(),256,[0,256], color = 'r') plt.xlim([0,256]) plt.legend(('cdf','histograma'), loc = 'upper left') plt.show() return hist
Example 23
Project: mmdetection Author: open-mmlab File: analyze_logs.py License: Apache License 2.0 | 5 votes |
def add_plot_parser(subparsers): parser_plt = subparsers.add_parser( 'plot_curve', help='parser for plotting curves') parser_plt.add_argument( 'json_logs', type=str, nargs='+', help='path of train log in json format') parser_plt.add_argument( '--keys', type=str, nargs='+', default=['bbox_mAP'], help='the metric that you want to plot') parser_plt.add_argument('--title', type=str, help='title of figure') parser_plt.add_argument( '--legend', type=str, nargs='+', default=None, help='legend of each plot') parser_plt.add_argument( '--backend', type=str, default=None, help='backend of plt') parser_plt.add_argument( '--style', type=str, default='dark', help='style of plt') parser_plt.add_argument('--out', type=str, default=None)
Example 24
Project: mmdetection Author: open-mmlab File: coco_error_analysis.py License: Apache License 2.0 | 5 votes |
def makeplot(rs, ps, outDir, class_name, iou_type): cs = np.vstack([ np.ones((2, 3)), np.array([.31, .51, .74]), np.array([.75, .31, .30]), np.array([.36, .90, .38]), np.array([.50, .39, .64]), np.array([1, .6, 0]) ]) areaNames = ['allarea', 'small', 'medium', 'large'] types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN'] for i in range(len(areaNames)): area_ps = ps[..., i, 0] figure_tile = iou_type + '-' + class_name + '-' + areaNames[i] aps = [ps_.mean() for ps_ in area_ps] ps_curve = [ ps_.mean(axis=1) if ps_.ndim > 1 else ps_ for ps_ in area_ps ] ps_curve.insert(0, np.zeros(ps_curve[0].shape)) fig = plt.figure() ax = plt.subplot(111) for k in range(len(types)): ax.plot(rs, ps_curve[k + 1], color=[0, 0, 0], linewidth=0.5) ax.fill_between( rs, ps_curve[k], ps_curve[k + 1], color=cs[k], label=str(f'[{aps[k]:.3f}]' + types[k])) plt.xlabel('recall') plt.ylabel('precision') plt.xlim(0, 1.) plt.ylim(0, 1.) plt.title(figure_tile) plt.legend() # plt.show() fig.savefig(outDir + f'/{figure_tile}.png') plt.close(fig)
Example 25
Project: tensorflow-DeepFM Author: ChenglongChen File: main.py License: MIT License | 5 votes |
def _plot_fig(train_results, valid_results, model_name): colors = ["red", "blue", "green"] xs = np.arange(1, train_results.shape[1]+1) plt.figure() legends = [] for i in range(train_results.shape[0]): plt.plot(xs, train_results[i], color=colors[i], linestyle="solid", marker="o") plt.plot(xs, valid_results[i], color=colors[i], linestyle="dashed", marker="o") legends.append("train-%d"%(i+1)) legends.append("valid-%d"%(i+1)) plt.xlabel("Epoch") plt.ylabel("Normalized Gini") plt.title("%s"%model_name) plt.legend(legends) plt.savefig("./fig/%s.png"%model_name) plt.close() # load data
Example 26
Project: Random-Erasing Author: zhunzhong07 File: logger.py License: Apache License 2.0 | 5 votes |
def plot(self, names=None): names = self.names if names == None else names numbers = self.numbers for _, name in enumerate(names): x = np.arange(len(numbers[name])) plt.plot(x, np.asarray(numbers[name])) plt.legend([self.title + '(' + name + ')' for name in names]) plt.grid(True)
Example 27
Project: Random-Erasing Author: zhunzhong07 File: logger.py License: Apache License 2.0 | 5 votes |
def plot(self, names=None): plt.figure() plt.plot() legend_text = [] for logger in self.loggers: legend_text += plot_overlap(logger, names) legend_text = ['WRN-28-10+Ours (error 17.65%)', 'WRN-28-10 (error 18.68%)'] plt.legend(legend_text, loc=0) plt.ylabel('test error (%)') plt.xlabel('epoch') plt.grid(True)
Example 28
Project: deep-learning-note Author: wdxtub File: utils.py License: MIT License | 5 votes |
def semilogy(x_vals, y_vals, x_label, y_label, x2_vals=None, y2_vals=None, legend=None): plt.xlabel(x_label) plt.ylabel(y_label) plt.semilogy(x_vals, y_vals) if x2_vals and y2_vals: plt.semilogy(x2_vals, y2_vals, linestyle=':') plt.legend(legend) plt.show()
Example 29
Project: fullrmc Author: bachiraoun File: run.py License: GNU Affero General Public License v3.0 | 5 votes |
def load_and_plot_constraints_benchmark(): benchmark = np.loadtxt(fname='benchmark_constraints_time.dat') tried = np.loadtxt(fname='benchmark_constraints_tried.dat') accepted = np.loadtxt(fname='benchmark_constraints_accepted.dat') # plot keys = open('benchmark_constraints_time.dat').readlines()[0].split("#")[1].split() minY = 0 maxY = 0 for idx in range(1,len(keys)): style = '-' if keys[idx] == 'all': style = '.-' # mean accepted meanAccepted = int( sum(benchmark[:,0]*accepted[:,idx])/sum(benchmark[:,0]) ) plt.plot(benchmark[:,0], benchmark[:,idx], style, label=keys[idx]+" (%i)"%meanAccepted) minY = min(minY, min(benchmark[:,idx]) ) maxY = max(minY, max(benchmark[:,idx]) ) # annotate tried(accepted) for i, txt in enumerate( accepted[:,-1] ): T = 100*float(tried[i,-1])/float(tried[1,1]) A = 100*float(accepted[i,-1])/float(tried[1,1]) plt.gca().annotate( "%.2f%% (%.2f%%)"%(T,A), #"%i (%i)"%( int(tried[i,-1]),int(txt) ), xy = (benchmark[i,0],benchmark[i,-1]), rotation=90, horizontalalignment='center', verticalalignment='bottom') # show plot plt.legend(frameon=False, loc='upper left') plt.xlabel("Number of atoms per group") plt.ylabel("Time per step (s)") plt.gcf().patch.set_facecolor('white') # set fig size #figSize = plt.gcf().get_size_inches() #figSize[1] = figSize[1]+figSize[1]/2. #plt.gcf().set_size_inches(figSize, forward=True) plt.ylim((None, maxY+0.3*(maxY-minY))) # save plt.savefig("benchmark_constraint.png") # plot plt.show()
Example 30
Project: fullrmc Author: bachiraoun File: run.py License: GNU Affero General Public License v3.0 | 5 votes |
def load_and_plot_steps_benchmark(constraint="all", groupSize=13): benchmark = np.loadtxt(fname='benchmark_%sSteps_%iGroupSize_time.dat'%(constraint,groupSize) ) tried = np.loadtxt(fname='benchmark_%sSteps_%iGroupSize_tried.dat'%(constraint,groupSize) ) accepted = np.loadtxt(fname='benchmark_%sSteps_%iGroupSize_accepted.dat'%(constraint,groupSize) ) # plot benchmark plt.plot(benchmark[:,0], benchmark[:,1]) minY = min(benchmark[:,1]) maxY = max(benchmark[:,1]) # annotate tried(accepted) for i, txt in enumerate( accepted[:,-1] ): T = 100*float(tried[i,-1])/float(benchmark[i,0]) A = 100*float(accepted[i,-1])/float(benchmark[i,0]) plt.gca().annotate( "%.2f%% (%.2f%%)"%(T,A), #str(int(tried[i,-1]))+" ("+str(int(txt))+")", xy = (benchmark[i,0],benchmark[i,-1]), rotation=90, horizontalalignment='center', verticalalignment='bottom') # show plot plt.legend(frameon=False, loc='upper left') plt.xlabel("Number of steps") plt.ylabel("Time per step (s)") plt.gcf().patch.set_facecolor('white') # set fig size #figSize = plt.gcf().get_size_inches() #figSize[1] = figSize[1]+figSize[1]/2. #plt.gcf().set_size_inches(figSize, forward=True) plt.ylim((None, maxY+0.3*(maxY-minY))) # save plt.savefig("benchmark_steps.png") # plot plt.show() ########################################################################################## ##################################### RUN BENCHMARKS ###################################