Python matplotlib.pyplot.plot() Examples
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code examples of matplotlib.pyplot.plot().
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Example #1
Source File: data_augmentation.py From Sound-Recognition-Tutorial with Apache License 2.0 | 10 votes |
def demo_plot(): audio = './data/esc10/audio/Dog/1-30226-A.ogg' y, sr = librosa.load(audio, sr=44100) y_ps = librosa.effects.pitch_shift(y, sr, n_steps=6) # n_steps控制音调变化尺度 y_ts = librosa.effects.time_stretch(y, rate=1.2) # rate控制时间维度的变换尺度 plt.subplot(311) plt.plot(y) plt.title('Original waveform') plt.axis([0, 200000, -0.4, 0.4]) # plt.axis([88000, 94000, -0.4, 0.4]) plt.subplot(312) plt.plot(y_ts) plt.title('Time Stretch transformed waveform') plt.axis([0, 200000, -0.4, 0.4]) plt.subplot(313) plt.plot(y_ps) plt.title('Pitch Shift transformed waveform') plt.axis([0, 200000, -0.4, 0.4]) # plt.axis([88000, 94000, -0.4, 0.4]) plt.tight_layout() plt.show()
Example #2
Source File: util.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 8 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 #3
Source File: dataset.py From neural-combinatorial-optimization-rl-tensorflow with MIT License | 8 votes |
def visualize_2D_trip(self, trip): plt.figure(figsize=(30,30)) rcParams.update({'font.size': 22}) # Plot cities plt.scatter(trip[:,0], trip[:,1], s=200) # Plot tour tour=np.array(list(range(len(trip))) + [0]) X = trip[tour, 0] Y = trip[tour, 1] plt.plot(X, Y,"--", markersize=100) # Annotate cities with order labels = range(len(trip)) for i, (x, y) in zip(labels,(zip(X,Y))): plt.annotate(i,xy=(x, y)) plt.xlim(0,100) plt.ylim(0,100) plt.show() # Heatmap of permutations (x=cities; y=steps)
Example #4
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 8 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 #5
Source File: dataset.py From neural-combinatorial-optimization-rl-tensorflow with MIT License | 7 votes |
def visualize_2D_trip(self,trip,tw_open,tw_close): plt.figure(figsize=(30,30)) rcParams.update({'font.size': 22}) # Plot cities colors = ['red'] # Depot is first city for i in range(len(tw_open)-1): colors.append('blue') plt.scatter(trip[:,0], trip[:,1], color=colors, s=200) # Plot tour tour=np.array(list(range(len(trip))) + [0]) X = trip[tour, 0] Y = trip[tour, 1] plt.plot(X, Y,"--", markersize=100) # Annotate cities with TW tw_open = np.rint(tw_open) tw_close = np.rint(tw_close) time_window = np.concatenate((tw_open,tw_close),axis=1) for tw, (x, y) in zip(time_window,(zip(X,Y))): plt.annotate(tw,xy=(x, y)) plt.xlim(0,60) plt.ylim(0,60) plt.show() # Heatmap of permutations (x=cities; y=steps)
Example #6
Source File: plotFigures.py From fullrmc with GNU Affero General Public License v3.0 | 7 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 #7
Source File: helper.py From Stock-Price-Prediction with MIT License | 7 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 #8
Source File: __init__.py From EDeN with MIT License | 7 votes |
def plot_roc_curve(y_true, y_score, size=None): """plot_roc_curve.""" false_positive_rate, true_positive_rate, thresholds = roc_curve( y_true, y_score) if size is not None: plt.figure(figsize=(size, size)) plt.axis('equal') plt.plot(false_positive_rate, true_positive_rate, lw=2, color='navy') plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--') plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.ylim([-0.05, 1.05]) plt.xlim([-0.05, 1.05]) plt.grid() plt.title('Receiver operating characteristic AUC={0:0.2f}'.format( roc_auc_score(y_true, y_score)))
Example #9
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 7 votes |
def plot_evolution_results(hyp): # from utils.utils import *; plot_evolution_results(hyp) # Plot hyperparameter evolution results in evolve.txt x = np.loadtxt('evolve.txt', ndmin=2) f = fitness(x) weights = (f - f.min()) ** 2 # for weighted results fig = plt.figure(figsize=(12, 10)) matplotlib.rc('font', **{'size': 8}) for i, (k, v) in enumerate(hyp.items()): y = x[:, i + 5] # mu = (y * weights).sum() / weights.sum() # best weighted result mu = y[f.argmax()] # best single result plt.subplot(4, 5, i + 1) plt.plot(mu, f.max(), 'o', markersize=10) plt.plot(y, f, '.') plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters print('%15s: %.3g' % (k, mu)) fig.tight_layout() plt.savefig('evolve.png', dpi=200)
Example #10
Source File: display_methods.py From indras_net with GNU General Public License v3.0 | 7 votes |
def create_scats(self, varieties): self.scats = pd.DataFrame(columns=["x", "y", "color", "marker", "var"]) for i, var in enumerate(varieties): self.legend.append(var) (x_array, y_array) = self.get_arrays(varieties, var) if len(x_array) <= 0: # no data to graph! ''' I am creating a single "position" for an agent that cannot be seen. This seems to fix the issue of colors being missmatched in the occasion that a group has no agents. ''' x_array = [-1] y_array = [-1] elif len(x_array) != len(y_array): logging.debug("Array length mismatch in scatter plot") return color = get_color(varieties[var], i) marker = get_marker(varieties[var], i) scat = pd.DataFrame({"x": pd.Series(x_array), "y": pd.Series(y_array), "color": color, "marker": marker, "var": var}) self.scats = self.scats.append(scat, ignore_index=True, sort=False)
Example #11
Source File: utils.py From pruning_yolov3 with 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 #12
Source File: plot_utils.py From keras-anomaly-detection with 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 #13
Source File: plot_lfads.py From DOTA_models with Apache License 2.0 | 6 votes |
def plot_time_series(vals_bxtxn, bidx=None, n_to_plot=np.inf, scale=1.0, color='r', title=None): if bidx is None: vals_txn = np.mean(vals_bxtxn, axis=0) else: vals_txn = vals_bxtxn[bidx,:,:] T, N = vals_txn.shape if n_to_plot > N: n_to_plot = N plt.plot(vals_txn[:,0:n_to_plot] + scale*np.array(range(n_to_plot)), color=color, lw=1.0) plt.axis('tight') if title: plt.title(title)
Example #14
Source File: util.py From neural-fingerprinting with 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 #15
Source File: dataset.py From neural-combinatorial-optimization-rl-tensorflow with MIT License | 6 votes |
def visualize_sampling(self, permutations): max_length = len(permutations[0]) grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0 transposed_permutations = np.transpose(permutations) for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t city_indices, counts = np.unique(cities_t,return_counts=True,axis=0) for u,v in zip(city_indices, counts): grid[t][u]+=v # update grid with counts from the batch of permutations # plot heatmap fig = plt.figure() rcParams.update({'font.size': 22}) ax = fig.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(grid, interpolation='nearest', cmap='gray') plt.colorbar() plt.title('Sampled permutations') plt.ylabel('Time t') plt.xlabel('City i') plt.show()
Example #16
Source File: classification.py From Kaggler with MIT License | 6 votes |
def plot_roc_curve(y, p): fpr, tpr, _ = roc_curve(y, p) plt.plot(fpr, tpr) plt.plot([0, 1], [0, 1], color='navy', linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate')
Example #17
Source File: recall.py From mmdetection with Apache License 2.0 | 6 votes |
def plot_iou_recall(recalls, iou_thrs): """Plot IoU-Recalls curve. Args: recalls(ndarray or list): shape (k,) iou_thrs(ndarray or list): same shape as `recalls` """ if isinstance(iou_thrs, np.ndarray): _iou_thrs = iou_thrs.tolist() else: _iou_thrs = iou_thrs if isinstance(recalls, np.ndarray): _recalls = recalls.tolist() else: _recalls = recalls import matplotlib.pyplot as plt f = plt.figure() plt.plot(_iou_thrs + [1.0], _recalls + [0.]) plt.xlabel('IoU') plt.ylabel('Recall') plt.axis([iou_thrs.min(), 1, 0, 1]) f.show()
Example #18
Source File: recall.py From mmdetection with Apache License 2.0 | 6 votes |
def plot_num_recall(recalls, proposal_nums): """Plot Proposal_num-Recalls curve. Args: recalls(ndarray or list): shape (k,) proposal_nums(ndarray or list): same shape as `recalls` """ if isinstance(proposal_nums, np.ndarray): _proposal_nums = proposal_nums.tolist() else: _proposal_nums = proposal_nums if isinstance(recalls, np.ndarray): _recalls = recalls.tolist() else: _recalls = recalls import matplotlib.pyplot as plt f = plt.figure() plt.plot([0] + _proposal_nums, [0] + _recalls) plt.xlabel('Proposal num') plt.ylabel('Recall') plt.axis([0, proposal_nums.max(), 0, 1]) f.show()
Example #19
Source File: tlib.py From TOPFARM with GNU Affero General Public License v3.0 | 6 votes |
def dist_from_segment(P1, P2, P3): """ Calculate the distance of a point P3 from a segment defined by [P1,P2] :param P1: ndarray([2]) or [2] :param P2: ndarray([2]) or [2] :param P3: ndarray([2]) or [2] :return dist: float """ l2P2P1 = l2_dist(P2,P1) if l2P2P1 == 0: return dist(P1,P3) x1, y1 = P1 x2, y2 = P2 x3, y3 = P3 u = ((x3-x1)*(x2-x1) + (y3-y1)*(y2-y1)) / l2P2P1 x = x1 + u*(x2-x1) y = y1 + u*(y2-y1) if u > 1.0: x, y = P2 if u < 0.0: x, y = P1 #plot([x3, x],[y3, y],'k--') return dist([x,y], P3)
Example #20
Source File: Flight Analysis.py From Cheapest-Flights-bot with MIT License | 6 votes |
def task_3_IQR(flight_data): plot=plt.boxplot(flight_data['Price'],patch_artist=True) for median in plot['medians']: median.set(color='#fc0004', linewidth=2) for flier in plot['fliers']: flier.set(marker='+', color='#e7298a') for whisker in plot['whiskers']: whisker.set(color='#7570b3', linewidth=2) for cap in plot['caps']: cap.set(color='#7570b3', linewidth=2) for box in plot['boxes']: box.set(color='#7570b3', linewidth=2) box.set(facecolor='#1b9e77') plt.matplotlib.pyplot.savefig('task_3_iqr.png') clean_data=[] for index,row in flight_data.loc[flight_data['Price'].isin(plot['fliers'][0].get_ydata())].iterrows(): clean_data.append([row['Price'],row['Date_of_Flight']]) return pd.DataFrame(clean_data, columns=['Price', 'Date_of_Flight'])
Example #21
Source File: utils.py From pruning_yolov3 with GNU General Public License v3.0 | 6 votes |
def plot_images(imgs, targets, paths=None, fname='images.jpg'): # Plots training images overlaid with targets imgs = imgs.cpu().numpy() targets = targets.cpu().numpy() # targets = targets[targets[:, 1] == 21] # plot only one class fig = plt.figure(figsize=(10, 10)) bs, _, h, w = imgs.shape # batch size, _, height, width bs = min(bs, 16) # limit plot to 16 images ns = np.ceil(bs ** 0.5) # number of subplots for i in range(bs): boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T boxes[[0, 2]] *= w boxes[[1, 3]] *= h plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0)) plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-') plt.axis('off') if paths is not None: s = Path(paths[i]).name plt.title(s[:min(len(s), 40)], fontdict={'size': 8}) # limit to 40 characters fig.tight_layout() fig.savefig(fname, dpi=200) plt.close()
Example #22
Source File: plot_3.py From cs294-112_hws with 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 #23
Source File: simulate_sin.py From deep-learning-note with 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 #24
Source File: utils.py From pruning_yolov3 with 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 #25
Source File: dataset.py From neural-combinatorial-optimization-rl-tensorflow with MIT License | 5 votes |
def visualize_sampling(self,permutations): max_length = len(permutations[0]) grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0 transposed_permutations = np.transpose(permutations) for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t city_indices, counts = np.unique(cities_t,return_counts=True,axis=0) for u,v in zip(city_indices, counts): grid[t][u]+=v # update grid with counts from the batch of permutations # plot heatmap fig = plt.figure() rcParams.update({'font.size': 22}) ax = fig.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(grid, interpolation='nearest', cmap='gray') plt.colorbar() plt.title('Sampled permutations') plt.ylabel('Time t') plt.xlabel('City i') plt.show() # Heatmap of attention (x=cities; y=steps)
Example #26
Source File: run.py From fullrmc with 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 #27
Source File: run.py From fullrmc with 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 ###################################
Example #28
Source File: utils.py From Deep_Learning_Weather_Forecasting with Apache License 2.0 | 5 votes |
def plot_prediction(x, y_true, y_pred, input_ruitu=None): """Plots the predictions. Arguments --------- x: Input sequence of shape (input_sequence_length, dimension_of_signal) y_true: True output sequence of shape (input_sequence_length, dimension_of_signal) y_pred: Predicted output sequence (input_sequence_length, dimension_of_signal) """ plt.figure(figsize=(12, 3)) output_dim = x.shape[-1]# feature dimension for j in range(output_dim): past = x[:, j] true = y_true[:, j] pred = y_pred[:, j] if input_ruitu is not None: ruitu = input_ruitu[:, j] label1 = "Seen (past) values" if j==0 else "_nolegend_" label2 = "True future values" if j==0 else "_nolegend_" label3 = "Predictions" if j==0 else "_nolegend_" label4 = "Ruitu values" if j==0 else "_nolegend_" plt.plot(range(len(past)), past, "o-g", label=label1) plt.plot(range(len(past), len(true)+len(past)), true, "x--g", label=label2) plt.plot(range(len(past), len(pred)+len(past)), pred, "o--y", label=label3) if input_ruitu is not None: plt.plot(range(len(past), len(ruitu)+len(past)), ruitu, "o--r", label=label4) plt.legend(loc='best') plt.title("Predictions v.s. true values v.s. Ruitu") plt.show()
Example #29
Source File: plot_utils.py From keras-anomaly-detection with MIT License | 5 votes |
def plot_training_history(history): if history is None: return plt.plot(history['loss']) plt.plot(history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper right') plt.show()
Example #30
Source File: tlib.py From TOPFARM with GNU Affero General Public License v3.0 | 5 votes |
def plot(self): plot(self.polygon[:,0], self.polygon[:,1], '--') plot(self.X[self.In].flatten(), self.Y[self.In].flatten(), 'k.')