def example_data():

    data = {

        'column names':

            ['OC1', 'OC2', 'OC3'],

        'data1':

            [[1.00, 0.00, 1.00, 1.00, 0.00, 1.06, 1.01, 1.00, 1.00],

             [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]],

                }

    return data

以上定义的'data1'中数据比较少，可以直接写在代码中， 请问如何把它换成保存在txt文件中比较大的数据：

 

我直接导入数据并且替换， 如下：

def example_data():

d1 = np.loadtxt('d1.txt')

d2 = np.loadtxt('d2.txt'),

d3 ...;

d4....

    data = {

        'column names':

            ['OC1', 'OC2', 'OC3'],

        'data1':

            [d1, d2, d3, d4],

                }

    return data

结果调用的时候出现格式错误：

    raise ValueError("x and y must have same first dimension")

ValueError: x and y must have same first dimension

请把代码和数据贴全。

 

"""

Example of creating a radar chart (a.k.a. a spider or star chart) [1]_.

 

Although this example allows a frame of either 'circle' or 'polygon', polygon

frames don't have proper gridlines (the lines are circles instead of polygons).

It's possible to get a polygon grid by setting GRIDLINE_INTERPOLATION_STEPS in

matplotlib.axis to the desired number of vertices, but the orientation of the

polygon is not aligned with the radial axes.

 

.. [1] http://en.wikipedia.org/wiki/Radar_chart

"""

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():

    filepath = 'C:\\Users\\Jingsong\\Desktop\\My papers\\PARADE2011 paper\\Windrose plot'

    DATA = np.loadtxt(filepath + '\\' + 'All data.txt',dtype = np.object)

    #Julianday CO NOx O3 HCHO Vaisala(WD) Vaisala(WS)

    data = np.delete(DATA,0,axis=0)

    k1 = len(data)

    utc = data[:,0]

    co = data[:,1]

    nox = data[:,2]    

    o3 = data[:,3]

    hcho = data[:,4]    

    wd = data[:,5]

    ws = data[:,6]

 

    UTC, WD, WS, CO, NOx, O3 = [], [], [], [], [],[]

    f1, f2, f3, f4 = 30, 8, 30, 100

    for j in range(k1):

        if ws[j] != '--' and co[j] != '--' and o3[j] != '--' and nox[j] != '--':

            CO.append(float(co[j]))

            O3.append(float(o3[j]))

            NOx.append(float(nox[j]))

            UTC.append(float(utc[j]))

            WD.append(float(wd[j]))

            WS.append(float(ws[j]))

        else :

            pass

 

    data = {

        'column names':

#            ['W','WSW','SW','SSW','S','SSE','SE','ESE','E', 'ENE', 'NE', 'NNE','N', 'NNW', 'NW', 'WNW'],

            ['N','N-N-W','N-W','W-N-W','W','W-S-W','S-W','S-S-W','S','S-S-E','S-E','E-S-E','E','E-N-E','N-E','N-N-E'

            ],

        'Mean (ppb)': # CO, NOx, O3

            [

            [102.24627786,106.83650667,99.92441288,96.53708956,90.24190571,90.71115166,97.72157486,

            104.4235838,113.2356681,114.58009237,114.84519222,112.92359114,109.30011623,107.12493302,103.95084444,

            101.40956103],

 

            [1.80433923*f1,2.64418*f1,3.03536707*f1,2.50796932*f1,1.73060199*f1,1.58757234*f1,2.17394835*f1,2.60896209*f1,3.15702655*f1,

            3.58586803*f1,3.48038276*f1,2.8917116*f1,2.38614875*f1,2.40764455*f1,2.6135483*f1,2.39924822*f1],

 

            [41.03782609, 44.49,41.73207447,  40.31592105,  37.31870728,  36.05327068,

            37.84087008,  43.77360938,  54.12770742,  54.6471243,   55.18656,     55.96642317,

            52.49690426,  43.06090741,  35.09577,     35.81180734  ],

            ],         

 

        'Median (ppb)':

            [

            [96.22278,96.22278,94.77351,92.15701,84.86605,85.090805,92.95792,101.62886,

            111.48659,113.70175,114.727555,111.69604,106.81137,103.645705,100.900165,100.195495],

 

            [1.76005*f3,2.64418*f3,2.52785*f3,1.93465*f3,1.375345*f3,1.29202*f3,1.7407*f3,2.07194*f3,2.38818*f3,2.635375*f3,2.39237*f3,

             1.97503*f3,1.82236*f3,1.82236*f3,1.80325*f3,1.96315*f3], 

 

            [40.27, 44.49,   40.51,   38.21,   35.19,   34.545,  34.82,   38.08,   56.46,   55.58,

             55.575,  56.74,   53.635,  46.095,  40.005,  37.16],

 

             ],

 

        'Maximum (ppb)':

            [[131.33685,131.33685,164.48452,164.48452,164.0069,170.14479,170.14479,163.73189,

            161.9768,161.9768,179.63048,179.63048,155.65133,155.65133,148.27387,125.06671],

 

            [3.52831*f2,3.52831*f2,8.87297*f2,8.87297*f2,8.50828*f2,6.07696*f2,12.98087*f2,12.98087*f2,15.03495*f2,17.31412*f2,19.37536*f2,

            19.37536*f2,15.69124*f2,8.4555*f2,7.29886*f2,7.29886*f2],

 

            [50.49, 48.71,  74.04,  74.04,  78.58,  78.58,  78.55,  80.27,  87.24,  88.73,  88.73,  73.00,

            72.89, 70.57,  51.43,  52.05  ],

 

         ],

 

        'Minimum (ppb)':

            [

            [90.54955,92.94989,77.3248,77.3248,74.055,72.75713,72.75713,74.15136,75.91537,77.13579,  

            78.21124,85.17453,89.88097,87.87074,87.87074,87.36955],

 

            [0.59927*f4, .76005*f4,  0.49778*f4,  0.36868*f4,  0.14775*f4,  0.14775*f4,  0.17049*f4,  0.17049*f4,  0.20257*f4,

            0.32186*f4,  0.34364*f4,  0.34364*f4,  0.50515*f4,  0.65795*f4,  0.48625*f4,  0.43119*f4  ],

 

           [34.08, 40.27,   23.49,   22.13,   14.65,   13.0,     13.0,     14.57,   14.75,   14.17,

           14.17,   26.71,    9.467,   9.467,   9.317,   9.317   ],

 

 

           ],

          }

    return data

 

 

if __name__ == '__main__':

    N = 16

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

 

    data = example_data()

 

    spoke_labels = data.pop('column names')

    plt.figure(4).clf()

    fig = plt.figure(4,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])

        plt.rgrids(angle=11.25)

        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.plot(theta, d,  'o-')

            ax.fill(theta, d, facecolor=color, alpha=0.05)

        ax.set_varlabels(spoke_labels)

 

    # add legend relative to top-left plot

    plt.subplot(2, 2, 1)

    labels = ('CO', 'NOx', 'O3')

    legend = plt.legend(labels, loc=(1.1, -0.2), labelspacing=0.2)

    plt.setp(legend.get_texts(), fontsize='12')

 

#    plt.figtext(0.5, 0.965, 'windrose plot',

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

    plt.show()

代码如上，请问如何把 

data = { 'Mean (ppbv)': # CO, NOx, O3} 中定义的3组数据替换成 np.loadtxt导入的数据：

即：

CO.append(float(co[j]))

O3.append(float(o3[j]))

NOx.append(float(nox[j]))