# ！/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
# import cv2
from matplotlib import pyplot as plt
import matplotlib.colors as mc
import colorsys
[docs]def lighten_color(color, amount=0.5, alpha=1):
"""
Lightens the given color by multiplying (1-luminosity) by the given amount.
Input can be matplotlib color string, hex string, or RGB tuple.
url :
https://stackoverflow.com/questions/37765197/darken-or-lighten-a-color-in-matplotlib
Parameters
----------
color : str or tuple
color to lighten
amount : float
amount to lighten the color. Value less than 1 produces a lighter color,
value greater than 1 produces a darker color.
alpha : float
alpha value of the color. Default is 1. The alpha value is a float
between 0 and 1.
Returns
-------
tuple
modified color in RGBA tuple (float values in the range 0-1).
Examples:
>> lighten_color('g', 0.3, 1)
(0.5500000000000002, 0.9999999999999999, 0.5500000000000002, 1)
>> lighten_color('#F034A3', 0.6, 0.5)
(0.9647058823529411, 0.5223529411764707, 0.783529411764706, 0.5)
>> lighten_color((.3,.55,.1), 0.5)
(0.6365384615384615, 0.8961538461538462, 0.42884615384615377, 1)
"""
try:
c = mc.cnames[color]
except:
c = color
c = colorsys.rgb_to_hls(*mc.to_rgb(c))
color_lightened = colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
return (*color_lightened, alpha)
[docs]def para_plot():
"""
This function is used to describe the parameters of the plot.
"""
import matplotlib as mpl
plt.style.use(['science', 'high-vis', 'grid'])
params = {
'axes.labelsize': 12,
'font.size': 12,
'legend.fontsize': 10,
'xtick.labelsize': 12,
'ytick.labelsize': 12,
'text.usetex': False,
}
font = {'family': 'normal', 'weight': 'normal'}
plt.rc('font', **font)
plt.rcParams["font.family"] = "Times New Roman"
mpl.rcParams.update(params)
[docs]def vert_sub_plot(num_plots, vspace, x, y, labels):
"""
This function is used to plot multiple subplots vertically.
Parameters
----------
num_plots : int
The number of subplots.
vspace : float
The vertical space between subplots.
x : numpy array
The x-axis data. The shape of x should be (num_points, num_plots).
y : numpy array
The y-axis data. The shape of y should be (num_points, num_plots).
labels : list
The labels of subplots.
Returns
-------
fig : matplotlib.figure.Figure
The figure object.
"""
para_plot()
fig, axs = plt.subplots(1, num_plots, sharey=True)
# Remove horizontal space between axes
fig.subplots_adjust(wspace=vspace)
for i in range(num_plots):
axs[i].plot(x[:, i], y[:, i], label=labels[i], linewidth=2)
# axs[i].set_yticks(np.arange(0, 1.0, 0.2))
# axs[i].set_xticks(np.arange(0, 2.0, 0.5))
# axs[i].set_ylim(0, 1)
axs[i].tick_params(axis='both', which='both', length=0)
axs[0].set_ylabel("Normalized distance")
# axs[1].set_xlabel("Density")
# plt.title('Density', x=-0.65, y=- 0.15, fontsize=12)
# plt.legend(loc='upper right', bbox_to_anchor=(1.0, 1.0), ncol=1)
return fig
# def plot_on_img(x, y, backgroundImg, labels=[], save=False):
# """
# This function is used to plot the image.
# Parameters
# ----------
# x,y: numpy array
# if x.shape[1]>1, 一个背景多个图
# labels: list of string
# , 多个图的legend
# img:
# a image as the background
# save: bool
# if True, save the image, default is False.
# Returns
# -------
# None.
# """
# img = cv2.imread(backgroundImg)
# imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# # img_bin = cv2.threshold(imgray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) # cv2.threshold (源图片, 阈值, 填充色, 阈值类型)
# fig = plt.figure(figsize=(16 / 2.54, 9.5 / 2.54))
# ax = fig.add_axes([0.12, 0.1, 0.85, 0.83])
# ax.imshow(imgray, cmap='gray', vmin=0, vmax=255)
# ##ax.imshow(img_bin[1], origin='lower', cmap='gray', vmin=0, vmax=255)
# # TODO 一个背景图多个曲线图
# x_shape, y_shape = len(np.shape(x)), len(np.shape(y))
# if x_shape == 2 or y_shape == 2:
# pass
# elif x_col == 1 and y_col == 1:
# # Append values to the end: np.append(arr, values, axis=None)
# x = np.append(x, [x[0]], axis=0)
# y = np.append(y, [y[0]], axis=0)
# ax.plot(x, y, '-.', label=labels, color='r')
# else:
# print('Only 1D or 2D (for multi curve plots) numpy array is accepted for x and y.')
# plt.show()
[docs]def xy_interp(*axis_list, num=100, raw=False):
"""
Interpolate the axis_list to the same x-axis and calculate the mean y-axis
for all the input x-y pairs (midline).
TODO: check what happens if the range of x-axis is not the same for all the input axis_list.
Notes
-----
[algorithm - How to interpolate a line between two other lines in python]
(https://stackoverflow.com/questions/49037902/how-to-interpolate-a-line-between-two-other-lines-in-python/49041142#49041142)
Parameters
----------
axis_list : list of np.ndarray
Each element is a 2D array with shape (n, 2), where n is the number of points.
The first column is x-axis and the second column is y-axis.
num : int, optional
The number of points to interpolate. The default is 100.
raw : bool, optional
If True, return the raw interpolated axis_list. The default is False.
Returns
-------
mid : np.ndarray
The interpolated midline with shape (n, 2), where n is the number of points.
The first column is x-axis and the second column is y-axis.
raw_interp : np.ndarray
The raw interpolated axis_list with shape (n, m), where n is the number of points
and m is the number of input axis_list. The first m columns are x-axis and the
last m columns are y-axis.
Examples
--------
>>> x1 = np.linspace(0, 10, 10)
>>> y1 = np.linspace(0, 15, 10)
>>> x2 = np.linspace(0, 10, 20)
>>> y2 = np.linspace(0, 12, 20)
>>> x3 = np.linspace(0, 9, 30)
>>> y3 = np.linspace(0, 18, 30)
>>> interp(np.vstack((x1, y1)).T, np.vstack((x2, y2)).T, np.vstack((x3, y3)).T)
"""
min_max_xs = [(min(axis[:, 0]), max(axis[:, 0])) for axis in axis_list]
# TODO : The interpolated range of x-axis is not the same for all the input axis_list.
# 1. Interpolate the axis_list to the same x-axis: [maximun min_x, minimun max_x].
# 2. Calculate the mean y-axis for all the input x-y pairs (midline)
new_axis_xs = [np.linspace(min_x, max_x, num) for min_x, max_x in min_max_xs]
new_axis_ys = [np.interp(new_x_axis, axis[:, 0], axis[:, 1])
for axis, new_x_axis in zip(axis_list, new_axis_xs)]
midx = [np.mean([new_axis_xs[axis_idx][i]
for axis_idx in range(len(axis_list))]) for i in range(num)]
midy = [np.mean([new_axis_ys[axis_idx][i]
for axis_idx in range(len(axis_list))]) for i in range(num)]
mid = np.vstack((midx, midy)).T
print(np.array(new_axis_xs).shape, np.array(new_axis_ys).shape)
raw_interp = np.vstack((new_axis_xs[0], new_axis_ys))
if raw:
return mid, raw_interp
else:
return mid
if __name__ == '__main__':
img = './test/imagePlot/trans0000.tif'
data = './test/imagePlot/4_1_XY_Coordinates.csv'
coordinate = np.loadtxt(open(data, "rb"), delimiter=",",
skiprows=1, usecols=(0, 1))
imagePlot(coordinate[:, 0], coordinate[:, 1], img)
# vSubPlots()
t = np.arange(0.0, 2.0, 0.01).reshape(-1, 1)
s1 = np.sin(2 * np.pi * t).reshape(-1, 1)
s2 = np.exp(-t).reshape(-1, 1)
s3 = s1 * s2.reshape(-1, 1)
s = np.hstack((s1, s2, s3))
t = np.hstack((t, t, t))
labels = ["Global KDE", 'MW-KDE (5)', 'MW-KDE (10)']
fig = vSubPlots(3, 0, t, s, labels)
fig.savefig("image.jpg", dpi=600)