matplotlib 画雷达图

2014-07-21 10:35:47 1.2k 阅读 AI 写同款 · GEO 优化AI 选题数据监测一键分发 ›

AI量化投资开源平台——Qlib（微矿）的学习使用笔记（随缘更新） Qlib 涵盖了量化投资的全过程，为用户的 AI 算法提供了高性能的底层基础架构，从框架设计上让用户可以更容易地应用 AI 算法来辅助解决量化投资的各个关键问题（例如 Alpha 预测，风险预测，市场动态性建模）。本文旨在记录一些面向初学者的基础内容。阅读详情

请问有没有人用python的matplotlib画过雷达图？

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Jetson Nano摄像头选型与配置全攻略：CSI与USB接口实战指南在嵌入式视觉与边缘计算项目中，摄像头作为核心传感器，其选型与配置直接影响系统性能。CSI（Camera Serial Interface）和USB（Universal Serial Bus）是两种主流接口，前者通过MIPI CSI-2协议直接与SoC通信，具备低延迟、高带宽特性，适合实时性要求高的应用；后者基于通用串行总线标准，即插即用，通用性强。在NVIDIA Jetson Nano平台上，合理选择摄像头接口并优化驱动配置，能充分发挥其GPU加速优势，提升图像处理流水线效率。本文聚焦Jetson Nano 阅读详情

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shanshan2410 2015-07-15

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编译不通过啊

惟愿莲心不染尘 2014-07-25

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看起来很好玩

nieoding 2014-07-25

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import numpy as np



import matplotlib.pyplot as plt

from matplotlib.path import Path

from matplotlib.spines import Spine

from matplotlib.projections.polar import PolarAxes

from matplotlib.projections import register_projection







def radar_factory(num_vars, frame='circle'):

    """Create a radar chart with `num_vars` axes.



    This function creates a RadarAxes projection and registers it.



    Parameters

    ----------

    num_vars : int

        Number of variables for radar chart.

    frame : {'circle' | 'polygon'}

        Shape of frame surrounding axes.



    """

    # calculate evenly-spaced axis angles

    theta = 2*np.pi * np.linspace(0, 1-1./num_vars, num_vars)

    # rotate theta such that the first axis is at the top

    theta += np.pi/2



    def draw_poly_patch(self):

        verts = unit_poly_verts(theta)

        return plt.Polygon(verts, closed=True, edgecolor='k')



    def draw_circle_patch(self):

        # unit circle centered on (0.5, 0.5)

        return plt.Circle((0.5, 0.5), 0.5)



    patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}

    if frame not in patch_dict:

        raise ValueError('unknown value for `frame`: %s' % frame)



    class RadarAxes(PolarAxes):



        name = 'radar'

        # use 1 line segment to connect specified points

        RESOLUTION = 1

        # define draw_frame method

        draw_patch = patch_dict[frame]



        def fill(self, *args, **kwargs):

            """Override fill so that line is closed by default"""

            closed = kwargs.pop('closed', True)

            return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)



        def plot(self, *args, **kwargs):

            """Override plot so that line is closed by default"""

            lines = super(RadarAxes, self).plot(*args, **kwargs)

            for line in lines:

                self._close_line(line)



        def _close_line(self, line):

            x, y = line.get_data()

            # FIXME: markers at x[0], y[0] get doubled-up

            if x[0] != x[-1]:

                x = np.concatenate((x, [x[0]]))

                y = np.concatenate((y, [y[0]]))

                line.set_data(x, y)



        def set_varlabels(self, labels):

            self.set_thetagrids(theta * 180/np.pi, labels)



        def _gen_axes_patch(self):

            return self.draw_patch()



        def _gen_axes_spines(self):

            if frame == 'circle':

                return PolarAxes._gen_axes_spines(self)

            # The following is a hack to get the spines (i.e. the axes frame)

            # to draw correctly for a polygon frame.



            # spine_type must be 'left', 'right', 'top', 'bottom', or `circle`.

            spine_type = 'circle'

            verts = unit_poly_verts(theta)

            # close off polygon by repeating first vertex

            verts.append(verts[0])

            path = Path(verts)



            spine = Spine(self, spine_type, path)

            spine.set_transform(self.transAxes)

            return {'polar': spine}



    register_projection(RadarAxes)

    return theta





def unit_poly_verts(theta):

    """Return vertices of polygon for subplot axes.



    This polygon is circumscribed by a unit circle centered at (0.5, 0.5)

    """

    x0, y0, r = [0.5] * 3

    verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta]

    return verts





def example_data():

    #The following data is from the Denver Aerosol Sources and Health study.

    #See  doi:10.1016/j.atmosenv.2008.12.017

    #

    #The data are pollution source profile estimates for five modeled pollution

    #sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical species.

    #The radar charts are experimented with here to see if we can nicely

    #visualize how the modeled source profiles change across four scenarios:

    #  1) No gas-phase species present, just seven particulate counts on

    #     Sulfate

    #     Nitrate

    #     Elemental Carbon (EC)

    #     Organic Carbon fraction 1 (OC)

    #     Organic Carbon fraction 2 (OC2)

    #     Organic Carbon fraction 3 (OC3)

    #     Pyrolized Organic Carbon (OP)

    #  2)Inclusion of gas-phase specie carbon monoxide (CO)

    #  3)Inclusion of gas-phase specie ozone (O3).

    #  4)Inclusion of both gas-phase speciesis present...

    data = {

        'column names':

            ['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO',

             'O3'],

        'Basecase':

            [[0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],

             [0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],

             [0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00],

             [0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00],

             [0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]],

        'With CO':

            [[0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00],

             [0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00],

             [0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00],

             [0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00],

             [0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]],

        'With O3':

            [[0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03],

             [0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00],

             [0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00],

             [0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95],

             [0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]],

        'CO & O3':

            [[0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01],

             [0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00],

             [0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00],

             [0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88],

             [0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]]}

    return data





if __name__ == '__main__':

    N = 9

    theta = radar_factory(N, frame='polygon')



    data = example_data()

    spoke_labels = data.pop('column names')



    fig = plt.figure(figsize=(9, 9))

    fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)



    colors = ['b', 'r', 'g', 'm', 'y']

    # Plot the four cases from the example data on separate axes

    for n, title in enumerate(data.keys()):

        ax = fig.add_subplot(2, 2, n+1, projection='radar')

        plt.rgrids([0.2, 0.4, 0.6, 0.8])

        ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),

                     horizontalalignment='center', verticalalignment='center')

        for d, color in zip(data[title], colors):

            ax.plot(theta, d, color=color)

            ax.fill(theta, d, facecolor=color, alpha=0.25)

        ax.set_varlabels(spoke_labels)



    # add legend relative to top-left plot

    plt.subplot(2, 2, 1)

    labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')

    legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)

    plt.setp(legend.get_texts(), fontsize='small')



    plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',

                ha='center', color='black', weight='bold', size='large')

    plt.show()

angel_su 2014-07-21

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google一下，应该有一堆范例...

matplotlib 画雷达图

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