215 lines
8.0 KiB
Python
215 lines
8.0 KiB
Python
"""
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主要是一些方便画图的函数
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"""
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import numpy as np
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import matplotlib.lines as lines
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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from matplotlib import font_manager
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import glob, os
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from matplotlib.colors import LinearSegmentedColormap, rgb_to_hsv, hsv_to_rgb, to_rgb
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def set_tformat():
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import datetime
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import matplotlib.dates as mdates
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import matplotlib.units as munits
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converter = mdates.ConciseDateConverter()
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munits.registry[np.datetime64] = converter
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munits.registry[datetime.date] = converter
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munits.registry[datetime.datetime] = converter
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base_dir = os.environ.get("zhouxu", os.environ.get("HOME"))
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font_dir = f"{base_dir}/Su-A-important/font/"
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if os.path.isdir(font_dir):
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for font in glob.glob(font_dir+"*"):
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if os.path.isfile(font):
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font_manager.fontManager.addfont(font)
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def set_font(font="Source Han Sans SC"):
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"""
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font: Source Han Sans SC, SimHei, Sarasa Mono T SC, Inconsolata, ex.
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a = font_manager.FontProperties(fname=f"{base_dir}/Su-A-important/font/sarasa-monoT-sc-regular.ttf")
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a.get_name()
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"""
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plt.rcParams["font.sans-serif"]=[font]
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plt.rcParams["axes.unicode_minus"]=False
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def get_font_name(font_file):
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"""
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font_file: path of font file like "./sarasa-monoT-sc-regular.ttf"
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"""
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font = font_manager.FontProperties(fname=font_file)
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font_name = font.get_name()
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return font_name
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def set_font_file(font_file):
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"""
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font_file: path of font file like "./sarasa-monoT-sc-regular.ttf"
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"""
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font_name = get_font_name(font_file)
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font_manager.fontManager.addfont(font_file)
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set_font(font_name)
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def set_font_size(size=9):
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plt.rcParams["font.size"] = size
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def point_transform(tar_prj, src_prj, x, y):
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"""将一个点从一个投影转换到另一个投影"""
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return tar_prj.inverted().transform(src_prj.transform([x, y]))
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def point_fig2axes(ax, fig, x, y):
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"""将Figure坐标下的点转换到Axes坐标"""
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return point_transform(ax.transAxes, fig.transFigure, x, y)
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def point_fig2data(ax, fig, x, y):
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"""将Figure坐标下的点转换到Data坐标"""
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return point_transform(ax.transData, fig.transFigure, x, y)
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def point_axes2fig(fig, ax, x, y):
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"""将Axes坐标下的点转换到Figure坐标"""
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return point_transform(fig.transFigure, ax.transAxes, x, y)
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def point_axes2data(tar_ax, src_ax, x, y):
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"""将Axes坐标下的点转换到Data坐标"""
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return point_transform(tar_ax.transData, src_ax.transAxes, x, y)
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def point_data2axes(tar_ax, src_ax, x, y):
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"""将Data坐标下的点转换到Axes坐标"""
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return point_transform(tar_ax.transAxes, src_ax.transData, x, y)
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def point_data2fig(fig, ax, x, y):
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"""将Data坐标下的点转换到Figure坐标"""
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return point_transform(fig.transFigure, ax.transData, x, y)
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def connect_axs(fig, ax1, ax2, color='k', lw=1):
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"""用于将ax1左边ax2的数据范围在ax1中绘制成子框, 并且绘制两个框的连线"""
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# 绘制ax1子框到ax2左上角的连线
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verts = [
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point_data2fig(fig, ax1, *point_axes2data(ax2, ax2, 1, 1)),
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point_axes2fig(fig, ax2, 0, 1),
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]
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line = lines.Line2D([x[0] for x in verts], [x[1] for x in verts], color=color, lw=lw)
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fig.add_artist(line)
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# 绘制ax1子框到ax2左下角的连线
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verts = [
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point_data2fig(fig, ax1, *point_axes2data(ax2, ax2, 1, 0)),
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point_axes2fig(fig, ax2, 0, 0),
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]
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line = lines.Line2D([x[0] for x in verts], [x[1] for x in verts], color=color, lw=lw)
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fig.add_artist(line)
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point_list = []
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# 绘制ax1中的边框
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for x, y in [[0, 0], [0, 1], [1, 1], [1, 0], [0, 0]]:
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x_temp, y_temp = point_axes2data(ax2, ax2, x, y)
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x_temp, y_temp = point_data2fig(fig, ax1, x_temp, y_temp)
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point_list.append((x_temp, y_temp))
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ax1.plot([x[0] for x in point_list], [x[1] for x in point_list], transform=fig.transFigure, color=color, lw=lw)
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def add_right_cax(ax, pad=0.01, width=0.01):
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'''
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在一个ax右边追加与之等高的cax.
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pad是cax与ax的间距,width是cax的宽度.
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'''
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axpos = ax.get_position()
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caxpos = mpl.transforms.Bbox.from_extents(
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axpos.x1 + pad,
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axpos.y0,
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axpos.x1 + pad + width,
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axpos.y1
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)
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cax = ax.figure.add_axes(caxpos)
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return cax
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def get_boundary(file, pad=0.01, h_pad=None, w_pad=None):
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"""
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get parameters for clipping white space in figure
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Parameters:
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file: str or mpl.figure.Figure or np.ndarray
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Something like figure file path or url, or object of figure.
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pad: int or float, optional
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Int in px, float in ratio, additional white space from boundary.
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h_pad: int or float, optional
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Like pad, for horizontal, default equal to pad.
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v_pad: int or float, optional
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Like pad, for vertical, default equal to pad.
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Returns:
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(dx, dy, xs, ys)
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Example: figure[ys:ys+dy, xs:xs+dx]
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Update:
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2024-08-13 01:20:10 Sola Source code
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2024-08-13 09:24:01 Sola fix bug add fig.canvas.draw()
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"""
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if isinstance(file, str):
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data = np.array(plt.imread(file)).astype(float)
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elif isinstance(file, mpl.figure.Figure):
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file.canvas.draw()
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data = np.array(file.canvas.buffer_rgba()).astype(float)
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elif isinstance(file, np.ndarray):
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data= file
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ny, nx = data.shape[0:2]
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h_pad = pad if h_pad is None else h_pad
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h_pad = int(h_pad*ny) if isinstance(h_pad, float) else h_pad
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w_pad = pad if w_pad is None else w_pad
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w_pad = int(w_pad*nx) if isinstance(w_pad, float) else w_pad
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if np.max(data) > 1:
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data /= 255
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if data.ndim == 3:
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data = np.mean(data, axis=-1)
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data_y, data_x = np.mean(data, axis=1) < 1, np.mean(data, axis=0) < 1
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xs, xe = np.argmax(data_x), nx - np.argmax(data_x[::-1])
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ys, ye = np.argmax(data_y), ny - np.argmax(data_y[::-1])
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dx, dy = xe - xs, ye - ys
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return dx + 2*w_pad, dy + 2*h_pad, xs - w_pad, ys - h_pad
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def interp_color_linear(color1, color2, n=16):
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color1_rgb, color2_rgb = np.array(color1), np.array(color2)
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result = interp_linear(np.array(color1_rgb), np.array(color2_rgb), n)
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return result
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def interp_linear(value1, value2, n=16):
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return np.linspace(1, 0, n)[:, None] @ value1[None, :] + np.linspace(0, 1, n)[:, None] @ value2[None, :]
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def interp_color_hsv(color1_rgb, color2_rgb, direction="nearest", n=16):
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"""
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furthest
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if direction == "nearest":
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if abs(h1 - h2) < 0.5:
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interp
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else:
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min(h1, h2) += 1
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interp % 1
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else direction == "furthest":
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if abs(h1 - h2) < 0.5:
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min(h1, h2) += 1
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interp % 1
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else:
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interp
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if abs(h1 - h2) > 0.5 => interp => furthest
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"""
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color1_hsv, color2_hsv = rgb_to_hsv(color1_rgb), rgb_to_hsv(color2_rgb)
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h_distance = abs(color1_hsv[0] - color2_hsv[0])
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if ((direction == "nearest") and (h_distance < 0.5)) or ((direction == "furthest") and (h_distance > 0.5)):
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result = interp_linear(color1_hsv, color2_hsv, n)
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elif ((direction == "nearest") and (h_distance > 0.5)) or ((direction == "furthest") and (h_distance < 0.5)):
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if color1_hsv[0] > color2_hsv[0]:
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color2_hsv[0] += 1
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else:
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color1_hsv[0] += 1
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result = interp_linear(color1_hsv, color2_hsv, n)
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result[result > 1] -= 1
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return hsv_to_rgb(result)
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hgt_1 = LinearSegmentedColormap.from_list("hgt #1", np.concatenate(
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[
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interp_color_hsv(to_rgb("#aff0e9"), to_rgb("#ffffb2"), direction="nearest", n=37),
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interp_color_hsv(to_rgb("#ffffb2"), to_rgb("#008040"), direction="nearest", n=37),
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interp_color_linear(to_rgb("#008040"), to_rgb("#f7c823"), n=37),
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interp_color_hsv(to_rgb("#f7c823"), to_rgb("#800000"), direction="nearest", n=37),
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interp_color_linear(to_rgb("#800000"), to_rgb("#7a3f12"), n=37),
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interp_color_linear(to_rgb("#7a3f12"), to_rgb("#b9b9b9"), n=37),
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interp_color_linear(to_rgb("#b9b9b9"), to_rgb("#fdfdfe"), n=37),
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],
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axis=0
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)) |