fig.tight_layout() plt.savefig("dailysubmissionsandcomments.png", facecolor="#222222") Word Clouds For creating word clouds we will use the wordcloud library, which makes it very easy. The first thing to do is to load the tokens or entities datasets. # No special flags required. df = pd...
# all plotting functions return matplotlib figures from npyx.plot import plot_wvf, get_peak_chan u=234 # plot waveform, 2.8ms around templates center, on 16 channels around peak channel # (the peak channel is found automatically, no need to worry about finding it) fig = plot_wvf(dp, u...
importtiktokenfromprevious_chaptersimportgenerate_text_simpledeftext_to_token_ids(text,tokenizer):encoded=tokenizer.encode(text,allowed_special={'<|endoftext|>'})encoded_tensor=torch.tensor(encoded).unsqueeze(0)# add batch dimensionreturnencoded_tensordeftoken_ids_to_text(token_ids,tokenizer):flat=t...
# 需要导入模块: from matplotlib.colors import LinearSegmentedColormap [as 别名]# 或者: from matplotlib.colors.LinearSegmentedColormap importfrom_list[as 别名]defdemo_compositing(im_r, im_g, im_b, im_rgb):fig = plt.figure(3) grid = ImageGrid(fig,222, nrows_ncols = (2,2), axes...
fig.tight_layout() # Adjust layout to make room plot_name = "loss-plot-standalone.pdf" print(f"Plot saved as {plot_name}") plt.savefig(plot_name) # plt.show() def main(test_mode=False): ### # Print package versions ### print() pkgs = [ "matplotlib", # Plotting library ...
("Tokens seen") fig.tight_layout() # Adjust layout to make room plot_name = "loss-plot-standalone.pdf" print(f"Plot saved as {plot_name}") plt.savefig(plot_name) # plt.show() def main(test_mode=False): ### # Print package versions ### print() pkgs = [ "matplotl...
# Context manager to handle plotting class create_figure(object): def __init__(self, name): self.name = name def __enter__(self): self.fig = plt.figure() print(f"Generating {self.name} as Figure {self.fig.number}") return self.fig def __exit__(self, type, value, traceback)...
savefig(fig) pp.close() # If I don't need to edit vector paths I save the file as a # PNG so I can import it directly into Photoshop: plt.savefig('./savename.png', format='png', dpi=600, pad_inches=0, transparent=True)
# all plotting functions return matplotlib figures from npyx.plot import plot_wvf, get_peak_chan u=234 # plot waveform, 2.8ms around templates center, on 16 channels around peak channel # (the peak channel is found automatically, no need to worry about finding it) fig = plot_wvf(dp, u...
print(f"Plotting {component} velocity hor_plane for height {self.z[z_idx]} at time {self.time[t_idx]}") plane = self.get_plane(z_idx, t_idx, component = 'u') if ax is None: fig, ax = plt.subplots() im = ax.pcolor(self.x, self.y, plane) ax.set_xlabel('X [m]') ...