def add_colorbar(powers_ax, im, cb_ticks=[], cb_ticks_font_size=12): from mpl_toolkits.axes_grid1.inset_locator import inset_axes axins = inset_axes(powers_ax, width="5%", height="100%", loc=5, bbox_to_anchor=(1.15, 0, 1, 1), bbox_transform=powers_ax.transAxes) cb = ...
#return_img = return_img.repeat(3,axis=0) return_img = torch.from_numpy(return_img).float().cuda() return return_data, return_yt, return_img def __len__(self): return len(self.img_path) class trainerData_cli(Dataset): def __init__(self, img_path, data, outcome, treatment, ...
if first_motor_ticks: self.motor_ticks = [] first_motor_ticks = False self.last_ticks = ticks self.motor_ticks.append( tuple([ticks[i]-self.last_ticks[i] for i in range(2)])) self.last_ticks = ticks # F is filtered trajectory. No time stamp is used. # File format: F x[i...
fig.add_subplot(231) ax1 = fig.add_subplot(2,3,1)# equivalent but more generalfig.add_subplot(232, frameon=False)# subplot with no framefig.add_subplot(233, projection='polar')# polar subplotfig.add_subplot(234, sharex=ax1)# subplot sharing x-axis with ax1fig.add_subplot(235, faceco...
plt.xticks([]) # 去掉x轴的刻度 plt.yticks([]) # 去掉y轴的刻度 plt.imshow(img.astype('uint8')) plt.show() def write_pickle(list_info: list, file_name: str): with open(file_name, 'wb') as f: pickle.dump(list_info, f) def read_pickle(file_name: str) -> list: with open...
I would also point out that the StochRSI panel has y-ticks on both the left and right, which suggests that between 'stochrsi_K' and 'stochrsi_D', one of them may be using axes[7] and the other axes[6]. If this is what you want, fine. If not, then you can code specifically...
@emmanuelleone thing I didn't manage to tweak with this Barpolar example was the radial axis ticks. For some reason the values do not match the actual radii values, if you look at this particular example you plotted for instance the values go from 0-5 while they should range from 0-1...
get_xticklabels() plt.ylabel('ESP-IDF Release', size=12) ax.invert_yaxis() fig.autofmt_xdate() darkred_patch = mpatches.Patch(color=lts_service_color, label=service_period_label) red_patch = mpatches.Patch(color=lts_maintenance_color, label=maintenance_period_text) plt.setp(plt.yticks...
Let's write this down more formally, as ultimately we need a tractable loss function which our neural network needs to optimize. Let \(q(\mathbf{x}_0)\) be the real data distribution, say of "real images". We can sample from this distribution to get an image, \(\...
plt.yticks([]) plt.savefig(fig_save_name, bbox_inches='tight') plt.close() 501 changes: 501 additions & 0 deletions 501 Forecasting/TSLANet_Forecasting.py Show comments View file Edit file Delete file Load diff Large diffs are not rendered by default. 67 changes: 67 additions &...